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Abbreviations in Research: Common Errors in Academic Writing 

“Provided they are not obscure to the reader, abbreviations communicate more with fewer letters. Writers have only to ensure that the abbreviations they use are too well known to need any introduction, or that they are introduced and explained on their first appearance.”  

—From “The Cambridge Guide to English Usage” by Pam Peters 1  

David Crystal defines abbreviations as “a major component of the English writing system, not a marginal feature. The largest dictionaries of abbreviations contain well over half a million entries, and their number is increasing all the time.” 2 Students and researchers often use abbreviations in research writing to save space, especially when facing restrictions of page or word limits. Abbreviations in research are also used in place of long or difficult phrases for ease of writing and reading. Exactly how abbreviations in research writing should be used depends on the style guide you follow. For example, in British English, the period (or full stop) is omitted in abbreviations that include the first and last letters of a single word (e.g., “Dr” or “Ms”). But in American English, such abbreviations in writing are followed by a period (e.g., “Dr.” or “Ms.”).  

While using abbreviations in academic writing is a common feature in many academic and scientific papers, most journals prefer keeping their use to a minimum or restricting their use to standard abbreviations. As a general rule, all non-standard acronyms/abbreviations in research papers should be written out in full on first use (in both the abstract and the paper itself), followed by the abbreviated form in parentheses, as in the American Psychological Association (APA) style guide. 

Table of Contents

• Mistakes to avoid when using acronyms and abbreviations in research writing3 
• Tips to using abbreviations in research writing 

Frequently Asked Questions (FAQ)

Mistakes to avoid when using acronyms and abbreviations in research writing 3.

• Avoid opening a sentence with an abbreviation in research papers; write the word out. 
• Abbreviations such as a.m., p.m., B.C., and A.D. are never spelled out. Unless your style guide says otherwise, use lowercase or small capitals for a.m. and p.m. Use  capital letters  or small caps for B.C. and A.D. (the periods are optional).  
• Avoid RAS Syndrome:   RAS Syndrome stands for Redundant Acronym Syndrome…Syndrome. For example, DC Comics—DC already stands for “Detective Comics,” making Comics after DC redundant. 
• Avoid Alphabet Soup:   Alphabet soup refers to using too many abbreviations in academic writing. Do not abbreviate the words if their frequency of appearance in the document is less than three. 
• Do not follow acronyms with a period unless at the end of a sentence. 
• When pluralizing acronyms add a lowercase “s” at the end (“three ECGs”); acronyms can be made possessive with an apostrophe followed by a lowercase “s” (“DOD’s acknowledge”). 
• Acronyms are treated as singulars, even when they stand for plurals. Therefore, they require a singular verb (“NASA is planning to…”). 
• Articles “a” or “an” before an acronym should be based on the opening sound rather than the acronym’s meaning. This depends on whether they are pronounced as words or as a series of letters. Use “an” if a soft vowel sound opens the acronym; else, use “a.” For example, a NATO meeting; an MRI scan. 

Tips to using abbreviations in research writing

1. When to abbreviate: Using too many abbreviations in research papers can make the document hard to read. While it makes sense to abbreviate every long word, it’s best to abbreviate terms you use repeatedly. 

2. Acronyms and initialisms: Define all acronyms and initialisms on their first use by giving the full terminology followed by the abbreviation in brackets. Once defined, use the shortened version in place of the full term. 

3. Contractions: Using contractions (isn’t, can’t, don’t, etc.) in academic writing, such as a research paper, is usually not encouraged because it can make your writing sound informal. 

4. Latin abbreviations: Latin abbreviations in research are widely preferred as they contain much meaning in a tiny package. Most style manuals (APA, MLA, and Chicago) suggest limiting the use of Latin abbreviations in the main text. They recommend using etc. , e.g. , and i.e., in parentheses within the body of a text, but others should appear only in footnotes, endnotes, tables, and other forms of documentation. But APA allows using “ et al .” when citing works with multiple authors and v. in the titles of court cases. 

5. Capitalization: Abbreviations in writing are in full capital letters (COBOL, HTML, etc.). Exceptions include acronyms such as “radar,” “scuba,” and “lidar,” which have become commonly accepted words. 

6. Punctuation: Abbreviations in research can be written without adding periods between each letter. However, when shortening a word, we usually add a period as follows: 

Figure → Fig. 

Doctor → Dr. 

January → Jan. 

Note that units of measurement do not require a period after the abbreviation. But, to avoid confusion with the word “ in ,” we write “ inches ” as “ in. ” in documents. 

7. Create a list: Make a list of the abbreviations in research as you write. Adding such a list at the start of your document can give the reader and yourself an easy point of reference.  

References  

• Peters, P. The Cambridge Guide to English Usage. Cambridge University Press (2004).  
• Crystal, D. Spell it out: The singular story of English spelling (2013). 
• Nordquist, R. 10 Tips for Using Abbreviations Correctly (July 25, 2019). Retrieved from https://www.thoughtco.com/tips-for-using-abbreviations-correctly-1691738  

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Abbreviations in a research paper are shortened forms of words or phrases used to represent specific terms or concepts. They are employed to improve readability and conciseness, especially when there are strict word counts and terms are mentioned frequently throughout the paper. To ensure clarity, it is essential to define each abbreviation when it is first used in the research paper. This is typically done by providing the full term followed by its abbreviation in parentheses.

Some commonly used abbreviations in academic writing include e.g. (exempli gratia), i.e. (id est), et al. (et alia/et alii), etc. (et cetera), cf. (confer), and viz. (videlicet). Additionally, there are several subject-specific abbreviations that are known by and commonly used in a field of study. However, know that abbreviations may mean different things across different fields. This makes it important to consult style guides or specific guidelines provided by the academic institution or target publication to ensure consistent and appropriate use of abbreviations in your academic writing.

An acronym is an abbreviation formed from the initial letters of a series of words and is pronounced as a word itself. For example, NASA (National Aeronautics and Space Administration) and UNESCO (United Nations Educational, Scientific and Cultural Organization) are acronyms. An abbreviation is a shortened form of a word or phrase but they are usually pronounced as individual letters. Examples of abbreviations include “et al.” for “et alia/et alii” and “e.g.” for “exempli gratia.”

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• List of Abbreviations | Example, Template & Best Practices

List of Abbreviations | Example, Template & Best Practices

Published on 23 May 2022 by Tegan George . Revised on 25 October 2022.

A list of abbreviations is an alphabetical list of abbreviations that you can add to your thesis or dissertation. If you choose to include it, it should appear at the beginning of your document, just after your table of contents .

Abbreviation lists improve readability, minimising confusion about abbreviations unfamiliar to your reader. This can be a worthwhile addition to your thesis or dissertation if you find that you’ve used a lot of abbreviations in your paper.

If you only use a few abbreviations, you don’t necessarily need to include a list. However, it’s never a bad idea to add one if your abbreviations are numerous, or if you think they will not be known to your audience.

You can download our template below in the format of your choice to help you get started.

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• Table of contents

Example list of abbreviations

Best practices for abbreviations and acronyms, additional lists to include, frequently asked questions.

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There are a few rules to keep in mind about using abbreviations in academic writing. Here are a few tips.

• Acronyms are formed using the first letter of each word in a phrase. The first time you use an acronym, write the phrase in full and place the acronym in parentheses immediately after it. You can then use the acronym throughout the rest of the text.
• The same guidance goes for abbreviations: write the explanation in full the first time you use it, then proceed with the abbreviated version.
• If you’re using very common acronyms or abbreviations, such as UK or DNA, you can abbreviate them from the first use. If you’re in doubt, just write it out in full the first time.

As well as the list of abbreviations, you can also use a list of tables and figures and a glossary for your thesis or dissertation.

Include your lists in the following order:

• List of figures and tables
• List of abbreviations

As a rule of thumb, write the explanation in full the first time you use an acronym or abbreviation. You can then proceed with the shortened version. However, if the abbreviation is very common (like UK or PC), then you can just use the abbreviated version straight away.

Be sure to add each abbreviation in your list of abbreviations !

If you only used a few abbreviations in your thesis or dissertation, you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimising confusion about abbreviations unfamiliar to your reader.

A list of abbreviations is a list of all the abbreviations you used in your thesis or dissertation. It should appear at the beginning of your document, immediately after your table of contents . It should always be in alphabetical order.

An abbreviation is a shortened version of an existing word, such as Dr for Doctor. In contrast, an acronym uses the first letter of each word to create a wholly new word, such as UNESCO (an acronym for the United Nations Educational, Scientific and Cultural Organization).

Your dissertation sometimes contains a list of abbreviations .

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Using abbreviations in scientific papers

It’s time to know more about abbreviations in scientific papers and learn ways to avoid mistakes when using them.

The use of abbreviations in academic and scientific publications is common, but authors are often asked to keep their usage as brief as possible. 

They are usually limited to universal abbreviations for weights and measurements. We would like to provide some tips in this article on how to use abbreviations effectively in your writing. 

If you are going to use abbreviations in scientific papers , then you should pay attention to the following tips.

What do abbreviations in scientific papers mean?

Abbreviations are shortened versions of terms and phrases, such as kg for kilograms, CEO for chief executive officer and Dr. for doctors. The use of abbreviations is ideally suited to situations in which you wish to reduce the number of words the text contains. 

However, there is a tendency for abbreviations to be widely used in one field of study but unknown in another. It is important to use the article that corresponds to the pronounced form of the abbreviation 

Are abbreviations allowed in research papers and where do you put them?

Your paper should include a list of abbreviations at the beginning of each of the following segments: heading, abstract, text, and figure/table legends. 

A common rule of thumb is to write all non-standard abbreviations in their entirety on their first appearance both in abstracts and papers themselves. 

After the first mention of an abbreviation, it is essential that you use it frequently. Additionally, the format should be consistently followed throughout the paper. 

Abbreviations and acronyms: what’s the difference?

The terms abbreviation and acronym are both shorthand versions of words and phrases. While abbreviations shorten longer words (like Dr. or Prof.), acronyms use the first letter of each word in a phrase to create a new word (like NASA or FBI). 

There is a difference between abbreviations and acronyms, even though authors often use them the same way. An acronym, initialism, or other word contraction form is an abbreviation. 

Acronyms are abbreviations formed by condensing the first letters of multiple words into one. Although not all abbreviations are acronyms, all acronyms are abbreviations. Abbreviations and acronyms differ primarily in this regard.

The most common mistakes to avoid when using abbreviations

Abbreviation errors in academic publications are sometimes common. The following are a few ways you can avoid this from happening in the future. 

• It is usually advisable to define abbreviations only once when you decide to use them. Exceptions do exist, however. An abbreviation may be used at the beginning of a section in a report or chapter in a book.
• Having an inconsistent approach is the top mistake you can make. The journals will provide guidelines on how to submit your work, so please read them carefully. Generally, abbreviations in scholarly articles are introduced only after three or more instances of the term.
• It is important to use standard abbreviations if you are writing in a field that uses them – for instance, elements in the physical sciences are often abbreviated for word count constraints. Standard formatting should always be used (both spelling and case-sensitive formatting). Capitalization is typically used only for proper nouns.
• Remember that for well-known abbreviations, lowercase is recommended over uppercase for competing terms, if the same letters are used in other abbreviations in the manuscript.
• The abbreviation “et al.” can be confusing to use in scientific writing because it is often misspelt or misused. As the name suggests, this term means “and others”. In-text citations or references are often shortened with this abbreviation, and it can be used wherever it precedes a name in the text. 

Here is a list of some scientific abbreviations

And the list goes on.

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List of Abbreviations for a Thesis or Dissertation

• By DiscoverPhDs
• September 14, 2020

What are Abbreviations and Acronyms?

An abbreviation is a shortened version of a term or phrase, e.g. kg for kilogram or Dr. for doctor.

An acronym is a type of abbreviation constructed from the first letters of a term, e.g. FRP for Fibre Reinforced Polymer or STEM for Science, Technology, Engineering and Maths.

List of Abbreviations in a Thesis or Dissertation

If your thesis or dissertation contains several symbols or abbreviations, it would be beneficial to include a list of abbreviations to assist your reader. This is a list sorted in alphabetical order that gives their definitions.

This will not only help the reader better understand your research, but it will also improve the flow of your paper, as it prevents continually having to define abbreviations in your main text.

Where Does a List of Abbreviations Go?

When including a list of abbreviations, insert them near the start of the report after your table of contents. To make it clear that your document contains an abbreviated list, also add a separate heading to your table of contents.

Note: The page number for your list of abbreviations should continue from the page number that proceeds it; there is no need to reset it for this section.

Rules for Using Abbreviations and Acronyms

The first time you use an abbreviation or acronym, it is good practice to write out the full terminology or phrase followed by the abbreviation or acronym encased in parenthesis.

After defining an abbreviation or acronym for the first time in your main text, you no longer need to use the full term; for example:

Example of Acronyms in a Thesis or Dissertation

This allows the reader to understand your report without having to rely on the list of abbreviations; it is only there to help the reader if they forget what an abbreviation stands for and needs to look it up.

Note: In academic writing, abbreviations that are not listed should always be defined in your thesis text at their first appearance.

Abbreviated Exceptions

Very common abbreviations should not be included in your list because they needlessly overload your list with terms that your readers already know, which discourages them from using it.

Some examples of common abbreviations and acronyms that should not be included in your standard abbreviation list are USA, PhD , Dr. and Ltd. etc.

Example of List of Abbreviations for a Thesis or Dissertation

An example abbreviation list is as follows:

The above example has been extracted from here .

List of Symbols

You can add symbols and their definitions to your list of abbreviations, however, some people like to keep them separate, especially if they have many of them. While this format will come down to personal preference, most STEM students create a separate list of symbols and most non-STEM students incorporate them into their list of abbreviations.

Note: If you are writing your report to APA style, you will need to consider additional requirements when writing your list of abbreviations. You can find further information here .

Further Reading

Whether you’re writing a Ph.D. thesis or a dissertation paper, the following resources will also be of use:

• Title Page for an Academic Paper
• List of Appendices

Finding a PhD has never been this easy – search for a PhD by keyword, location or academic area of interest.

This post explains where and how to write the list of figures in your thesis or dissertation.

The Thurstone Scale is used to quantify the attitudes of people being surveyed, using a format of ‘agree-disagree’ statements.

Learning how to effectively collaborate with others is an important skill for anyone in academia to develop.

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An abstract and introduction are the first two sections of your paper or thesis. This guide explains the differences between them and how to write them.

A science investigatory project is a science-based research project or study that is performed by school children in a classroom, exhibition or science fair.

Freya’s in the final year of her PhD at the University of Leeds. Her project is about improving the precision of observations between collocated ground-based weather radar and airborne platforms.

Amy recently entered her third and final year of her PhD at the University of Strathclyde. Her research has focussed on young people’s understanding of mental health stigma in Scotland.

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Other APA Guidelines: Abbreviations

Basics of abbreviations.

Using abbreviations can be an effective way to avoid repeating lengthy, technical terms throughout a piece of writing, but they should be used sparingly to prevent your text from becoming difficult to read.

Many abbreviations take the form of acronyms or initialisms, which are abbreviations consisting of the first letter of each word in a phrase. Examples are National Institute of Mental Health (NIMH) and Better Business Bureau (BBB). Note that the abbreviation uses all capital letters, and there are no periods between the letters.

To use an abbreviation, write out the term or phrase on first use, followed by the abbreviation in parentheses. See these examples:

The patient had been diagnosed with traumatic brain injury (TBI) in March of the previous year. Walden students need to know how to cite information using the American Psychological Association (APA) guidelines.

Using an Abbreviation in a Draft

After introducing the abbreviation, use the abbreviation by itself, without parentheses, throughout the rest of the document.

The patient had been diagnosed with traumatic brain injury (TBI) in March of 2014. According to the Centers for Disease Control and Prevention (CDC, 2015), people with TBI often have difficulty with memory and concentration, physical symptoms such as headaches, emotional symptoms such as sadness and irritability, and difficulty falling asleep. Although the patient explained that she experienced frequent headaches and difficulty concentrating, she had not been regularly taking any medication for her TBI symptoms when she visited the clinic 6 months after her diagnosis.

Note: When introducing an abbreviation within a narrative citation, use a comma between the abbreviation and the year.

Making an Abbreviation Plural

Simply add an “s” to an abbreviation to make it plural. (Do not add an apostrophe.)

I work with five other RNs during a typical shift.

Note: RN is a commonly used acronym found in Merriam-Webster’s Dictionary , so it does not need to be introduced. See the “Exceptions to the Rules” section below for more information about commonly used abbreviations.

Exceptions to the Rules

There are a few exceptions to the basic rules:

• If you use the phrase three times or fewer, it should be written out every time. However, a standard abbreviation for a term familiar in its abbreviated form is clearer and more concise, even if it is used fewer than three times.
• Commonly used acronyms and abbreviations may not need to be written out. If an abbreviation appears as a word in Merriam-Webster’s Collegiate Dictionary , then it does not need to be written it out on first use. Examples include words such as IQ, REM, and HIV.
• Other than abbreviations prescribed by APA in reference list elements (e.g., “ed.” for “edition,” “n.d.” for “no date,” etc.), do not use abbreviations in the references list. For example, a source authored by the Centers for Disease Control and Prevention would not be abbreviated as CDC in the references list.
• If using an abbreviation for a unit of measure with a numerical value, you do not need to write the term out on first use. For example, instead of writing “12 grams;” you can simply use “12 g.” If, however, you use a unit of measure without a numerical value, write the term out (e.g., “several grams”).
• Abbreviations for time, common Latin terms, and statistical abbreviations also follow specific rules. See APA 7, Sections 6.28, 6.29, and 6.44 for more information.

United States and U.S.

In APA style, "United States" should always be spelled out when it is used as a noun or location.

Example: In the United States, 67% reported this experience.

United States can be abbreviated as "U.S." when it is used as an adjective.

Examples: U.S. population and U.S. Census Bureau.

Abbreviations Video

• APA Formatting & Style: Abbreviations (video transcript)

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The growth of acronyms in the scientific literature

Adrian barnett.

1 School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia

Zoe Doubleday

2 Future Industries Institute, University of South Australia, Adelaide, Australia

Associated Data

The analysis code and data to replicate all parts of the analyses and generate the figures and tables are available from GitHub:  https://github.com/agbarnett/acronyms  ( Barnett, 2020 ; copy archived at  https://github.com/elifesciences-publications/acronyms ). We welcome re-use and the repository is licensed under the terms of the MIT license.

The analysis code and data to replicate all parts of the analyses and generate the figures and tables are available from GitHub: https://github.com/agbarnett/acronyms (copy archived at https://github.com/elifesciences-publications/acronyms ). We welcome re-use and the repository is licensed under the terms of the MIT license. Data was originally downloaded from the PubMed Baseline Repository (March 23, 2020; https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/ ).

Some acronyms are useful and are widely understood, but many of the acronyms used in scientific papers hinder understanding and contribute to the increasing fragmentation of science. Here we report the results of an analysis of more than 24 million article titles and 18 million article abstracts published between 1950 and 2019. There was at least one acronym in 19% of the titles and 73% of the abstracts. Acronym use has also increased over time, but the re-use of acronyms has declined. We found that from more than one million unique acronyms in our data, just over 2,000 (0.2%) were used regularly, and most acronyms (79%) appeared fewer than 10 times. Acronyms are not the biggest current problem in science communication, but reducing their use is a simple change that would help readers and potentially increase the value of science.

Introduction

As the number of scientific papers published every year continues to grow, individual papers are also becoming increasingly specialised and complex ( Delanty, 1998 ; Bornmann and Mutz, 2015 ; Doubleday and Connell, 2017 ; Cordero et al., 2016 ; Plavén-Sigray et al., 2017 ). This information overload is driving a ‘knowledge-ignorance paradox’ whereby information increases but knowledge that can be put to good use does not ( Jeschke et al., 2019 ). Writing scientific papers that are clearer to read could help to close this gap and increase the usefulness of scientific research ( Freeling et al., 2019 ; Letchford et al., 2015 ; Heard, 2014 ; Glasziou et al., 2014 ).

One feature that can make scientific papers difficult to read is the widespread use of acronyms ( Sword, 2012 ; Pinker, 2015 ; Hales et al., 2017 ; Lowe, 2019 ), and many researchers have given examples of the overuse of acronyms, and highlighted the ambiguities, misunderstandings and inefficiencies they cause ( Fred and Cheng, 2003 ; Narod et al., 2016 ; Patel and Rashid, 2009 ; Pottegård et al., 2014 ; Weale et al., 2018 ; Parvaiz et al., 2006 ; Chang et al., 2002 ). For example, the acronym UA has 18 different meanings in medicine ( Lang, 2019 ). Box 1 contains four sentences from published papers that show how acronyms can hinder understanding.

Examples of sentences with multiple acronyms.

The four sentences below are taken from abstracts published since 2000, and reflect the increasing complexity and specialisation of science.

• "Applying PROBAST showed that ADO, B-AE-D, B-AE-D-C, extended ADO, updated ADO, updated BODE, and a model developed by Bertens et al were derived in studies assessed as being at low risk of bias.’ (2019)
• "Toward this goal, the CNNT, the CRN, and the CNSW will each propose programs to the NKF for improving the knowledge and skills of the professionals within these councils.’ (2000)
• ‘After their co-culture with HC-MVECs, SSc BM-MSCs underwent to a phenotypic modulation which re-programs these cells toward a pro-angiogenic behaviour.’ (2013)
• "RUN had significantly (p<0.05) greater size-adjusted CSMI and BSI than C, SWIM, and CYC; and higher size, age, and YST-adjusted CSMI and BSI than SWIM and CYC." (2002)

In this article we report trends in the use of acronyms in the scientific literature from 1950 to the present. We examined acronyms because they can be objectively identified and reflect changes in specialisation and clarity in writing.

We analysed 24,873,372 titles and 18,249,091 abstracts published between 1950 and 2019, which yielded 1,112,345 unique acronyms. We defined an acronym as a word in which half or more of the characters are upper case letters. For example, mRNA and BRCA1 are both acronyms according to this definition, but N95 is not because two of the three characters are not upper case letters.

We found that the proportion of acronyms in titles increased from 0.7 per 100 words in 1950 to 2.4 per 100 words in 2019 ( Figure 1 ); the proportion of acronyms in abstracts also increased, from 0.4 per 100 words in 1956 to 4.1 per 100 words in 2019. There was at least one acronym in 19% of titles and 73% of abstracts. Three letter acronyms (jokingly called TLAs) were also more popular than acronyms of two and four letters.

The proportion of acronyms (purple line) has risen steadily over time in abstracts both for acronyms that are letters and/or numbers (top left) or just letters (top right). Acronyms are generally less common in titles than abstracts, and the proportion in titles has been relatively stable since 2000, but there was an increase from 1960 to 2000 (bottom left and right). Three-character acronyms (blue lines) are more common than two-character acronyms (brown-orange lines) and four-character acronyms (olive green lines) in both titles and abstracts. A sufficient number of abstracts only became available from 1956. The spikes in titles for acronyms of length 2+ in 1952 and 1964 are because of the relatively small number of papers in those years, with over 78,000 papers being excluded in 1964 because the title was in capitals.

Figure 1—figure supplement 1.

Each line shows the trend after excluding up to the n most popular acronyms ( n  = 1, ..., 100). The darkest line is for n  = 1, and the lightest line is for n  = 100. The number of titles and journals in the early 1950s is much smaller, hence the more erratic trend for titles in that decade.

Figure 1—figure supplement 2.

Data for six article types (journal article, clinical trial, case report, comment, editorial, and other). The high proportion of acronyms in the 1950s and 1960s for ‘other’ is driven by a relatively large number of obituaries that include qualifications, such as FRCP (Fellow of the Royal College of Physicians) or DSO (Distinguished Service Order). The drop in the proportion of acronyms in 2019 for ‘clinical trials’ and ‘other’ may be due to a delay in papers from some journals appearing in PubMed.

Figure 1—figure supplement 3.

Using a truncated y−axis more clearly shows the upward trend in the use of acronyms in titles for all article types over time (by reducing the influence of ‘other’ in the 1950s and 1960s; see Figure 1—figure supplement 2 ).

The proportion of acronyms in titles has flattened since around the year 2000, whereas the proportion in abstracts continued to increase. Moreover, when the 100 most popular acronyms were removed, there was still a clear increase in acronym use over time ( Figure 1—figure supplement 1 ). Furthermore, the increase was visible in all the article types we studied (including articles, clinical trials, case reports, comments and editorials: Figure 1—figure supplement 2 ; Figure 1—figure supplement 3 ). Video 1 shows the top ten acronyms in titles for every year from 1950 to 2019, and Video 2 shows the top ten acronyms in abstracts over the same period.

There are 17,576 possible three-letter acronyms using the upper case letters of the alphabet. We found that 94% of these combinations had been used at least once. Strikingly, out of the 1.1 million acronyms analysed, we found that the majority were rarely used, with 30% occurring only once, and 49% occurring between two and ten time times. Only 0.2% of acronyms (just over 2,000) occurred more than 10,000 times. One year after their first use, only 11% of acronyms had been re-used in a different paper in the same journal. Longer acronyms were less likely to be re-used, with a 17% re-use for two-character acronyms, compared with just 8% for acronyms of five characters or longer. The time taken for acronyms to be re-used has also increased over time ( Figure 2 ), indicating that acronyms created today are less likely to be re-used than previously created acronyms.

The solid line is the estimated time in years for 10% of newly coined acronyms to be re-used in the same journal. 10% was chosen based on the overall percentage of acronyms being re-used within a year. Newly coined acronyms are grouped by year. The dotted lines show the 95% confidence interval for the time to re-use, which narrows over time as the sample size increases. The general trend is of an increasing time to re-use from 1965 onwards, which indicates that acronyms are being re-used less often. The relatively slow times to re-use in the 1950s and early 1960s are likely due to the very different mix of journals in that time.

DNA is by far the most common acronym and is universally recognised by scientists and the public ( Table 1 ). However, not all the top 20 may be so widely recognised, and it is an interesting individual exercise to test whether you, the reader, recognise them all. Six of the top 20 acronyms also have multiple common meanings in the health and medical literature, such as US and HR‚ although the meaning can usually be inferred from the sentence.

How many do you recognise?

In parallel with increasing acronym use, the average number of words in titles and abstracts has increased over time, with a steady and predominantly linear increase for titles, and a more nonlinear increase for abstracts ( Figure 3 ). The average title length increased from 9.0 words in 1950 to 14.6 words in 2019, and shows no sign of flattening. The average abstract length has also increased, from 128 words in 1962 to 220 words in 2019, and again this trend shows no sign of flattening. It is worth pointing out that these increases have happened despite the word and character limits that many journals place on the length of titles and abstracts.

The average title length has increased linearly between 1950 and 2019 (left). The average length of abstracts has also increased since 1960, except for a brief reduction in the late 1970s and a short period of no change after 2000 (right). A sufficient number of abstracts only became available from 1956. Note that the y-axes in the two panels are different, and that neither starts at zero, because we are interested in the relative trend.

Our results show a clear increase over time in the use of acronyms titles and abstracts ( Figure 1 ), with most acronyms being used less than 10 times. Titles and abstracts are also getting longer ( Figure 3 ), meaning readers are now required to read more content of greater complexity.

There have been many calls to reduce the use of acronyms and jargon in scientific papers (see, for example, Talk Medicine BMJ, 2019 , which recommends a maximum of three acronyms per paper), and many journal and academic writing guides recommend a sparing use of acronyms ( Sword, 2012 ). However, the trends we report suggest that many scientists either ignore these guidelines or simply emulate what has come before. Entrenched writing styles in science are difficult to shift ( Doubleday and Connell, 2017 ), and the creation of new acronyms has become an acceptable part of scientific practice, and for clinical trials is a recognised form of branding ( Pottegård et al., 2014 ).

We believe that scientists should use fewer acronyms when writing scientific papers. In particular, they should avoid using acronyms that might save a small amount of ink but do not save any syllables, such as writing HR instead of heart rate ( Pinker, 2015 ; Lang, 2019 ). This approach might also make articles easier to read and understand, and even help avoid potential confusion (as HR can also mean hazard ratio or hour). For more complex phrases with multiple syllables and specialist words, such as methylcyclopentadienyl manganese tricarbonyl (MMT), acronyms may ease reading and aid understanding, although MMT might mean methadone maintenance treatment to some readers.

It is difficult to make a general rule about which acronyms to keep and which to spell out. However, there is scope for journals to reduce the use of acronyms by, for example, only permitting the use of certain established acronyms (although the list of allowed acronyms would have to vary from journal to journal). In the future it might be possible, software permitting, for journals to offer two versions of the same paper, one with acronyms and without, so that the reader can select the version they prefer.

Our work shows that new acronyms are too common, and common acronyms are too rare. Reducing acronyms should boost understanding and reduce the gap between the information we produce and the knowledge that we use ( Jeschke et al., 2019 ) ‚ without 'dumbing down' the science. We suggest a second use for DNA: do not abbreviate.

Materials and methods

We use the word acronym to refer to acronyms (such as AIDS), initialisms (such as WHO) and abbreviations that include capital letters (such as BRCA). We use this broad definition because we were interested in shortened words that a reader is either expected to know or could potentially misunderstand. We did not define acronyms as those defined by the authors using parentheses, because many acronyms were not defined.

We defined an acronym as a word in which half or more of the characters are upper case letters. For example, mRNA is an acronym because it has three upper case letters out of four characters. Characters include numbers, so BRCA1 is also an acronym according to our definition, but N95 is not because two of the three characters are not upper case letters. (It should, however, be noted that N95 is an abbreviation for National Institute for Occupational Safety and Health mask that filters at least 95% of airborne particles). Our definition of an acronym was arrived at using trial and error based on the number of acronyms captured and missed.

We included common acronyms (such as AIDS) because it is difficult to make a simple ruling about what is common and hence well accepted. We instead used a sensitivity analysis that excluded the most common acronyms. We did not include acronyms that have become common words and are generally now not written in upper case letters (such as laser).

Data extraction

The data were extracted from the PubMed repository of health and medical journals provided by the National Library of Medicine. Data were downloaded between 14 and 22 April 2020. Although the PubMed repository includes papers going back to the year 1781, we used 1950 as the first year. This is because although acronyms have been around for 5,000 years, their use greatly increased after the Second World War ( Cannon, 1989 ) and there were a relatively small number of papers in PubMed prior to 1950. The details of the algorithm to extract acronyms are in Appendix 1.

Random checks of the algorithm

One researcher (AB) randomly checked 300 of each of the following by hand to verify our acronym-finding algorithm:

• Titles that were excluded
• Titles defined as having no acronyms
• Titles defined as having at least one acronym
• Abstracts defined as having no acronyms
• Abstracts defined as having at least one acronym

The numbers in Table 2 are the count of errors and estimated upper bound for the error percentage using a Bayesian calculation based on the 95th percentile of a beta distribution using the observed number of papers with and without errors. The average error rates were between 0.3% and 6.3%. Zero error rates are unlikely given the great variety of styles across journals and authors. Examples of acronyms missed by our algorithm include those with a relatively large number of lower case letters, numbers, symbols or punctuation (such as circRNA). Examples of acronyms wrongly included by our algorithm include words written in capitals for emphasis and the initials of someone's name appearing in the title as part of a Festschrift.

Exclusion reasons and numbers

Table 3 lists the most common reasons why papers were excluded from our analysis. Papers were excluded if they were not written in English, and this was the main exclusion reason for titles. Over 7 million papers had no abstract and around 10,000 had an abstract that was empty or just one word long. Over 298,000 titles and 112,000 abstracts had 60% or more of words in capital letters, making it difficult to distinguish acronyms. This 60% threshold for exclusion was found using trial and error.

Statistical analysis

We examined trends over time by plotting annual averages. The trends were the average number of words in the title and abstract, and the proportion of acronyms in abstracts and titles using word count as the denominator. To examine varied types of acronyms, we split the trends according to acronyms that were letters only compared with those using letters and numbers. We also examined trends according to the length of the acronym. We calculated 95% confidence intervals for the annual means, but in general these were very close to the mean because of the enormous sample size, and hence we do not present them.

We examined the re-use of acronyms after their first use by calculating the number of acronyms re-used in a different paper in the same journal up to one year after their first use. We used the same journal in an attempt to standardise by field and so reduce the probability of counting re-uses for different meanings (such as ED meaning emergency department in the field of emergency medicine and eating disorder in the field of psychology). We counted re-use in the title or abstract. We censored times according to the last issue of the journal. We examined whether re-use was associated with the length of the acronym.

All data management and analyses were made using R version 3.6.1 ( R Core Team, 2020 ).

Data and code availability

Limitations.

Our algorithm missed a relatively large number of acronyms that included symbols, punctuation and lower case letters. This means our estimates likely underestimate the total number of acronyms. We assumed this error is balanced over time, meaning our trends reflect a real increase in acronym use. We only examined abstracts and titles, not the main text. This is because we used an automated procedure to extract the data, and large numbers of main texts are only available for open access journals and only for recent years, making these data unsuitable for examining broad trends across the scientific literature. We took a broad look at overall trends and did not examine trends within journals or fields. However, our code and data are freely available, and hence other trends and patterns could be examined.

Acknowledgements

Computational resources and services were provided by the eResearch Office at Queensland University of Technology. We thank Ruth Tulleners for help with searching the literature and checking the data, and Angela Dean for comments on a draft.

Biographies

Adrian Barnett is in the School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia

Zoe Doubleday is in the Future Industries Institute, University of South Australia, Adelaide, Australia

Text processing algorithm

• The data were downloaded in eXtensible Markup Language (XML) format and read into R for processing. The complete R code is freely available on GitHub. The list of key processing steps in order are below. Unless otherwise stated, each step was applied to the title and abstract.
• Remove commonly used copyright sentences such as ‘This article is protected by copyright’ from the abstract.
• Extract the PubMed ID, date the paper was added to PubMed, title, abstract, journal name, article type, language, and number of authors.
• Remove copyright sections and sub-headings from the abstract (such as ‘BACKGROUND’).
• Remove systematic review registrations from the abstract.
• Exclude papers not in English.
• Exclude titles and abstracts that are empty.
• Exclude abstracts of 10 words or fewer that have the article type Published Erratum because they are usually just a citation/note.
• Remove Roman numerals from 1 to 30 (that is, from I to XXX). The upper limit of 30 was based on trial and error. Also remove Roman numerals suffixed with a lower case letter or ‘th’.
• Remove the chromosomes: XX, XY, XO, ZO, XXYY, ZW, ZWW, XXX, XXXX, XXXXX, YYYYY.
• Remove symbols, including mathematical symbols, spacing symbols, punctuation, currencies, trademarks, arrows, superscripts and subscripts.
• Remove common capitals from the title (such as WITHDRAWN, CORRIGENDUM and EDITORIAL).
• Remove sub-headings (such as SETTING and SUBJECTS) from the abstract. (This step is performed in addition to the sub-heading removal step mentioned above).
• Replace common units in the abstract with ‘dummy’ (for example, change ‘kg/m2’ to ‘dummy’) so that they are counted as one word during word counts.
• Remove full-stops in acronyms (so that, for example, W.H.O. becomes WHO).
• Replace all plurals by replacing "s " with a space. This removes plurals from acronyms: for example, EDs becomes ED.
• Break the title and abstract into words.
• Exclude the title or abstract if 60% or more of the words are in capitals as this is a journal style and makes it hard to find acronyms.
• Exclude abstracts with strings of four or more words in capitals. Trial and error showed that this was a journal style and it makes it hard to spot acronyms.
• Exclude the title if the first four words or last four words are in capitals. This is a journal style.
• Remove gene sequences defined as characters of six or more than only contain the letters A, T, C, G, U and p.
• Find and exclude citations to the current paper in the abstract, as these often have author initials which look like acronyms.
• Exclude titles of just one word.
• Exclude papers with a missing PubMed Date.
• Extract acronyms, defined as a word where half or more of the characters are upper case.
• Exclude acronyms longer than 15 characters, as these are often gene strings.

We did not include the following as acronyms:

• Common units of measurement like pH‚ hr or mL.
• Chemical symbols like Na or Ca.
• Acronyms that have become common words (such as laser).
• e.g. and i.e.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Peter Rodgers, eLife, United Kingdom.

Funding Information

This paper was supported by the following grants:

• National Health and Medical Research Council APP1117784 to Adrian Barnett.
• Australian Research Council FT190100244 to Zoe Doubleday.

Additional information

No competing interests declared.

Conceptualization, Software, Formal analysis, Validation, Visualization, Writing - original draft.

Conceptualization, Investigation, Writing - original draft.

Additional files

Transparent reporting form, data availability.

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Writing academically: Abbreviations

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Abbreviations and acronyms are shortened forms of words or phrases. Generally, abbreviations are not acceptable in academic writing (with some exceptions, see below) and acronyms are (providing they are used as shown below).

As academic writing is formal in style, most abbreviations should be avoided. Even the common ones shown below:

Some common ones to avoid

Avoid e.g. and i.e. , instead use for example and for instance .

Avoid etc . There isn't really an alternative, so rewrite the sentence.

Avoid dept , govt . Use  department , government .

Avoid NB , instead use note that .

Avoid vs  or v , instead use versus or against (except in Law reports or cases)

Some acceptable abbreviations

Titles such as Mr. Dr. Prof. are acceptable when using them in conjunction with the individual's name i.e. Dr. Smith.

Some Latin phrases

et al. (short form of et alia - and others is acceptable when giving in text citations with multiple authors. The full stop should always be included afterwards to acknowledge the abbreviation. It does not need to be italicised as it is in common usage.

ibid. (short form of ibidim - in the same place) is acceptable if using footnote references to indicate that a reference is the same as the previous one. Again, always include the full stop to acknowledge the abbreviation. It is the convention to italicise this as it is less commonly used.

sic (short form of sic erat scriptum - thus it was written). This is used to indicate there was an error in something you are quoting (either an interviewee or an author) and it is not a misquote. It is added in square brackets but is neither italicised nor followed by a full stop i.e.

"it'd be great if unis [sic] could develop a person's self-knowledge"

Acronyms are acceptable, but use the name in full on its first use in a particular document (e.g. an assignment), no matter how well known the acronym is. For example, on its first use in an essay you might refer to "the World Health Organisation (WHO)" - it would be fine to simply refer to "the WHO" for the remainder of the essay.

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There are a few common trends in abbreviating that you should follow when using MLA, though there are always exceptions to these rules. For a complete list of common abbreviations used in academic writing, see Appendix 1 in the MLA Handbook (9 th  ed.).

Uppercase letter abbreviations

Do not use periods or spaces in abbreviations composed solely of capital letters, except in the case of proper names:

unless the name is only composed of initials:

Lowercase letter abbreviations

Use a period if the abbreviation ends in a lowercase letter, unless referring to an Internet suffix, where the period should come before the abbreviation:

Note: Degree names are a notable exception to the lowercase abbreviation rule.

Use periods between letters without spacing if each letter represents a word in common lowercase abbreviations:

Other notable exceptions:

Abbreviations in citations

Condense citations as much as possible using abbreviations.

Time Designations

Remember to follow common trends in abbreviating time and location within citations. Month names longer than four letters used in journal and magazine citations should be abbreviated:

Geographic Names

Use geographic names of states and countries. Abbreviate country, province, and state names.

Scholarly Abbreviations

The MLA Handbook (9 th ed.) encourages users to adhere to the common scholarly abbreviations for both in-text citations and in the works-cited page. Here is the list of common scholarly abbreviations from Appendix 1 of the MLA Handbook (9 th ed.) with a few additions:

• anon. for anonymous
• app. for appendix
• bk. for book
• c. or ca. for circa
• ch. for chapter
• col. for column
• def. for definition
• dept. for department
• e.g. for example
• ed. for edition
• et al. for multiple names (translates to "and others")
• etc. for "and so forth"
• fig. for figure
• fwd. for foreword
• i.e. for that is
• jour. for journal
• lib. for library
• MS, MSS for manuscript(s)
• no. for number
• P for Press (used for academic presses)
• p. for page, pp. for pages
• par. for paragraph when page numbers are unavailable
• qtd. in for quoted in
• rev. for revised
• sec. or sect. for section
• ser. for series
• trans. for translation
• U for University (for example, Purdue U)
• UP for University Press (for example, Yale UP or U of California P)
• vers. for version
• var. for variant
• vol. for volume

Publisher Names

Cite publishers’ names in full as they appear on title or copyright pages. For example, cite the entire name for a publisher (e.g. W. W. Norton or Liveright Publishing).

Exceptions:

• Omit articles and business abbreviations (like Corp., Inc., Co., and Ltd.).
• Use the acronym of the publisher if the company is commonly known by that abbreviation (e.g. MLA, ERIC, GPO). For publishers who are not known by an abbreviation, write the entire name.
• Use only U and P when referring to university presses (e.g. Cambridge UP or U of Arkansas P)

For more information on scholarly abbreviations, see Appendix 1 of the MLA Handbook (9 th  ed.) . See also the following examples:

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Onsemble Community

Diversity, Equity, & Inclusion

Participants & Advocacy

Advarra Partner Network

Community Advocacy

Institutional Review Board (IRB)

Institutional Biosafety Committee (IBC)

Data Monitoring Committee (DMC)

Endpoint Adjudication (EAC)

GxP Services

Research Compliance & Site Operations

Professional Services

Coverage Analysis​

Budget Negotiation​

Calendar Build 

Technology Staffing

Clinical Research Site Training

Research-Ready Training

Virtual Investigator Meetings

Custom eLearning

Services for

Sponsors & CROs

Sites & Institutions

BioPharma & Medical Tech

Oncology Research

Decentralized Clinical Trials

Early Phase Research

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Electronic Consenting System

eSource and Electronic Data Capture

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Clinical Research Acronyms and Abbreviations You Should Know

We all know there are numerous acronyms and abbreviations used in clinical research. While some can be easily deciphered, others may take some searching to find their meaning. Particularly with the recent surge in electronic systems and regulations, it can be hard to keep track of necessary abbreviations and terms.

Whether you’re new to the clinical research world or need a refresher, here’s a condensed list of common acronyms and abbreviations you may come across.

AACI: Association of American Cancer Institutes

AAHRPP: Association for the Accreditation of Human Research Protection Programs

ABSA: Association of Biosafety and Biosecurity

ACRP: Association of Clinical Research Professionals

ACTS: Association for Clinical and Translational Science

ADME: Absorption, Distribution, Metabolism, and Elimination

ADR: Adverse Drug Reaction

AE: Adverse Event

ALCOA:  Attributable, Legible, Contemporaneous, Original, Accurate

AMC: Academic Medical Center

API: Active Pharmaceutical Ingredient

API: Application Program Interface

ARO: Academic Research Organization

ASCO: American Society of Clinical Oncology

ASCPT: American Society for Clinical Pharmacology and Therapeutics

ASGCT: American Society of Gene & Cell Therapy

BA/BE: Bioavailability/Bioequivalance

BLA: Biological Licensing Application

BSM: Biospecimen Management

caBIG: Cancer Biomedical

CAPA:  Corrective and Preventive Action

CAR-T: Chimeric Antigen Receptors and T cells

CBER: Center for Biologics Evaluation and Research

CBRN: California Board of Registered Nursing

CCEA: Complete, Consistent, Enduring, Available

CCOP: Community Clinical Oncology Program

CCR: Center for Cancer Research

CCSG: Cancer Center Support Grant

CCTO or CTO: Centralized Clinical Trials Office or Clinical Trials Office

CDASH:  Clinical Data Acquisition Standards Harmonization

CDER: Center for Drug Evaluation and Research

CDM: Clinical Data Management

Related article: “Improve Data Quality with 5 Fundamentals of Clinical Data Management”

CDP: Clinical Development Plan

CDRH: Center for Devices and Radiological Health

CDS: Clinical Data System

CDUS: Clinical Data Update System

CFR: Code of Federal Regulations

CMO: Contract Manufacturing Organization

CMS: Centers for Medicare & Medicaid Services

CRA: Clinical Research Associate

CRC: Clinical Research Coordinator

Related article: “Deciphering the CRC Career Path: Key Skills and Responsibilities”

CRF: Case Report Form

CRMS: Clinical Research Management System

CRO: Contract Research Organization

CRPC: Clinical Research Process Content

CSO:  Contract Safety Organization

CSR: Clinical Study Report

CTA: Clinical Trial Authorization

CTCAE: Common Terminology Criteria for Adverse Events

CTMS: Clinical Trial Management System

CTRP: Clinical Trials Reporting Program

CTSA: Clinical and Translational Science Award

DDI: Drug-Drug Interaction

DHHS: Department of Health and Human Services

DM: Data Manager

DMC: Data Monitoring Committee

DSMB: Data and Safety Monitoring Board

EC: Ethics Committee

eCOA: Electronic Clinical Outcome Assessment

eCRF: Electronic Case Report Form

EDC: Electronic Data Capture

Learn more about Advarra’s electronic data capture system, Advarra EDC .

EHR: Electronic Health Record

EMR: Electronic Medical Record

ePRO: Electronic Patient-Reported Outcomes

eTMF: Electronic Trial Master File

FAIR: Findable, Accessible, Interoperable, Reusable

FDA: Food and Drug Administration

FE: Food Effect

FIH: First In Human

FWA: Federalwide Assurance

GCP: Good Clinical Practice

GCRC: General Clinical Research Center

GDP:  Good Documentation Practice

GLP: Good Laboratory Practice

GMP: Good Manufacturing Practice

GVP: Good Pharmacovigilance Practice

HIPAA: Health Insurance Portability and Accountability Act

HRPP: Human Research Protection Program

IBC: Institutional Biosafety Committee

ICF: Informed Consent Form

ICH: International Council for Harmonization

IDE: Investigational Device Exemptions

IEC: Independent Ethics Committee

IHCRA: In House Clinical Research Associate

IIT: Investigator Initiated Trial

IND: Investigational New Drug (Application)

IP: Investigational Product

IRB: Institutional Review Board

ITT: Intent to Treat

IVRS: Interactive Voice Response System

IWRS: Interactive Web Response System

LTFU: Long Term Follow Up

LRAA: Local Regulatory Affairs Associate

MAC: Medicare Administrative Contractor

MAD: Multiple Ascending Dose

MCA: Medicare Coverage Analysis

Related webinar: Build a Better Budget: Using Medicare Coverage Analysis to Streamline Study Startup .

MRN: Medical Record Number

NCI: National Cancer Institute

NDA: New Drug Application

NHV: Normal Healthy Volunteer

NIH: National Institutes of Health

NLM: National Library of Medicine

OCT: Office of Clinical Trials

OHRP: Office for Human Research Protections

OSR: Outside Safety Report

PAC: Post Approval Commitments

PC: Protocol Coordinator

PD: Protocol Director

PHI:  Protected Health Information

PI: Principal Investigator

PK/PD: Pharmacokinetic/Pharmacodynamic

PRE: Prompt Reporting Event

PRMC: Protocol Review and Monitoring Committee

PRMS: Protocol Review and Monitoring System

QC: Quality Control

QCT: Qualifying Clinical Trial

QMS: Quality Management System

SAD: Single Ascending Dose

SAE: Serious Adverse Event

SC: Study Coordinator

SDR: Source Document Review (Also Source Data Review)

SDTM:  Study Data Tabulation Model

SDV: Source Document Verification

SIF: Site Investigator File

SMO: Site Management Organization

SOC: Standard of Care

SOE: Schedule of Events

SOP: Standard Operating Procedure

Related article: “Data Collection in Clinical Trials: 4 Steps for Creating an SOP”

SPOREs: Specialized Programs for Research Excellence

SRB: Scientific Review Board

SRC: Scientific Review Committee

SUSAR: Suspected Unexpected Serious Adverse Reaction

SVT: Subject Visit Template

TMF: Trial Master File (also eTMF)

TMO: Trial Management Organization

UADE: Unanticipated Adverse Device Effect

UADR: Unexpected Adverse Drug Reaction

UAP: Unanticipated Problem

Is there an abbreviation or acronym you see regularly in clinical research that isn’t listed here? Let us know .

To learn more about the fundamentals of clinical research, check out these free resources:

• Improve Data Quality with 5 Fundamentals of Clinical Data Management
• Beginner’s Guide to Clinical Trial Performance Metrics
• Deciphering the CRC Career Path: Key Skills and Responsibilities

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Meghan Hosely creates educational content for Advarra, such as blogs, eBooks, white papers, and more.

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• A Research Guide
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Common & Uncommon Abbreviations for Research Papers

• Common Abbreviations

Abbreviations of Degrees

Some common abbreviations.

For a complete list of Common Scholarly Abbreviations, please see Section 7.4 in the 6th edition of the MLA Handbook for Writers of Research Papers .

Note: When documenting sources using MLA style, the normal punctuation is omitted for degrees when used in parentheses, tables, works cited, footnotes, endnotes, etc. For example, B.A. is written as BA. Other abbreviations retain the periods if applicable, e.g. acad., bib., misc. Italics = Latin

Also, g reat post to read about  US and Canada Map .

Links to Common, Uncommon, and Specialized Abbreviations

Acronym Finder . Look up acronyms, abbreviations and their meanings.

Acronym Search . Your source for acronyms and abbreviations.

Common Abbreviations for posting messages on Bulletin Boards – from BabyCentre.co.uk. Examples: AKA = Also known as, LOL = Laughing out loud, BTW = By the way, CUL = See you later, Yw/Ty = You’re welcome/thank you. Emotions: 🙂 or 🙂 = Smiling (Happy), 🙁 or 🙁 = Frowning (Sad), 😮 = Shouting, xxooxxoo = Love (or hugs) & kisses. See also Shorthand and Meaning from Genealogy.com. (Scroll way down the page to view Shorthand). Examples: = I’m grinning, IMHO = in my humble opinion, FYI = for your information, FWIW = for what it’s worth, ROTFL = rolling on the floor laughing, WTG = way to go, Emotions: 😉 or 😉 = winking, 😀 or 😀 = laughing.

Common Abbreviations from Fact Monster for kids.

Common Abbreviations, Common Symbols, Acronyms for Organizations from Web-based Training Modules funded by the U.S. National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) Program.

Common Abbreviations from U.S. Department of State.

Common Abbreviations and Acronynms from AllEarsNet.com – Deb’s Unofficial Walt Disney World Information Guide.

Common Abbreviations in Writing.

Common Abbreviations Used in International Narcotics Control Strategy Report.

HyperWar – World War II on the WorldWideWeb – Abbreviations, Acronyms, Codewords, Terms .

Military Abbreviations and Acronyms of the US Armed Forces.

Also, great post to read concerning Abbreviations for Books of the Bible .

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Align Your Steps: Optimizing Sampling Schedules in Diffusion Models

Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sampling speed, relying on many sequential function evaluations through large neural networks. Sampling from DMs can be seen as solving a differential equation through a discretized set of noise levels known as the sampling schedule. While past works primarily focused on deriving efficient solvers, little attention has been given to finding optimal sampling schedules, and the entire literature relies on hand-crafted heuristics. In this work, for the first time, we propose Align Your Steps, a general and principled approach to optimizing the sampling schedules of DMs for high-quality outputs. We leverage methods from stochastic calculus and find optimal schedules specific to different solvers, trained DMs and datasets. We evaluate our novel approach on several image, video as well as 2D toy data synthesis benchmarks, using a variety of different solvers, and observe that our optimized schedules outperform previous handcrafted schedules in almost all experiments. Our method demonstrates the untapped potential of sampling schedule optimization, especially in the few-step synthesis regime.

Our optimized schedules can be used at inference time in a plug-and-play fashion. Please see our quickstart guide to get started with using our schedules in diffusers and the colab notebook for example code with Stable Diffusion 1.5 and SDXL.

The sampling schedule is iteratively optimized to reduce the discretization error. As the optimization proceeds, the generated images become sharper and more detailed.

Optimizing Sampling Schedules in Diffusion Models

Diffusion models (DMs) have proven themselves to be extremely reliable probabilistic generative models that can produce high-quality data. They have been successfully applied to applications such as image synthesis, image super-resolution, image-to-image translation, image editing, inpainting, video synthesis, text-to-3d generation, and even planning. However, sampling from DMs corresponds to solving a generative Stochastic or Ordinary Differential Equation (SDE/ODE) in reverse time and requires multiple sequential forward passes through a large neural network, limiting their real-time applicability.

Solving SDE/ODEs within the interval \([t_{min}, t_{max}]\) works by discretizing it into \(n\) smaller sub-intervals \(t_{min} = t_0 < t_1 < \dots < t_{n}=t_{max}\) called a sampling schedule, and numerically solving the differential equation between consecutive \(t_i\) values. Currently, most prior works adopt one of a handful of heuristic schedules, such as simple polynomials and cosine functions, and little effort has gone into optimizing this schedule. We attempt to fill this gap by introducing a principled approach for optimizing the schedule in a dataset and model specific manner, resulting in improved outputs given the same compute budget.

Assuming that \( P_{true} \) represents the distribution of running the reverse-time SDE (defined by the learnt model) exactly, and \( P_{disc} \) represents the distribution of solving it with Stochastic-DDIM and a sampling schedule, using the Girsanov theorem an upper bound can be derived for the Kullback-Leibler divergence between these two distributions (simplified; see paper for details) \[ D_{KL}(P_{true} || P_{disc}) \leq \underbrace{ \sum_{i=1}^{n} \int_{t_{i-1}}^{t_{i}} \frac{1}{t^3} \mathbb{E}_{x_t \sim p_t, x_{t_i} \sim p_{t_i | t}} || D_{\theta}(x_t, t) - D_{\theta}(x_{t_i}, t_i) ||_2^2 \ dt }_{= KLUB(t_0, t_1, \dots, t_n)} + constant \] A similar Kullback-Leibler Upper Bound (KLUB) can be found for other stochastic SDE solvers. Given this, we formulate the problem of optimizing the sampling schedule as minimizing the KLUB term with respect to its time discretization, i.e. the sampling scheduling. Monte-Carlo integration with importance sampling is used to estimate the expectation values and the schedule is optimized iteratively. We showcase the benefits of optimizing schedules on a 2D toy distribution (see visualization below).

Modeling a 2D toy distribution: Samples in (b), (c), and (d) are generated using 8 steps of SDE-DPM-Solver++(2M) with EDM, LogSNR, and AYS schedules, respectively. Each image consists of 100,000 sampled points.

Experimental Results

To evaluate the usefulness of optimized schedules, we performed rigorous quantitative experiments on standard image generation benchmarks (CIFAR10, FFHQ, ImageNet), and found that these schedules result in consistent improvements across the board in image quality (measured by FID) for a large variety of popular samplers. We also performed a user study for text-to-image models (specifically Stable Diffusion 1.5), and found that on average images generated with these schedules are preferred twice as much . Please see the paper for these results and evaluations.

Below, we showcase some text-to-image examples that illustrate how using an optimized schedule can generate images with more visual details and better text-alignment given the same number of forward evaluations (NFEs). We provide side-by-side comparisons between our optimized schedules against two of the most popular schedules used in practice (EDM and Time-Uniform). All images are generated with a stochastic ( casino ) or deterministic ( lock ) version of DPM-Solver++(2M) with 10 steps. Hover over the images for zoom-ins.

Stable Diffusion 1.5

casino Text prompt: "1girl, blue dress, blue hair, ponytail, studying at the library, focused" Model: Dreamshaper 8

casino Text prompt: "An enchanting forest path with sunlight filtering through the dense canopy, highlighting the vibrant greens and the soft, mossy floor"

casino Text prompt: "A digital Illustration of the Babel tower, 4k, detailed, trending in artstation, fantasy vivid colors"

casino Text prompt: "A glass-blown vase with a complex swirl of colors, illuminated by sunlight, casting a mosaic of shadows on a white table"

casino Text prompt: "A delicate glass pendant holding a single, luminous firefly, its glow casting warm, dancing shadows on the wearer's neck"

casino Text prompt: "A wise old owl wearing a velvet smoking jacket and spectacles, with a pipe in its beak, seated in a vintage leather armchair"

casino Text prompt: "A close-up portrait of a baby wearing a tiny spider-man costume, trending on artstation" Model: Dreamshaper 8

DeepFloyd-IF

casino Text prompt: "Capybara podcast neon sign"

casino Text prompt: "Long-exposure night photography of a starry sky over a mountain range, with light trails"

casino Text prompt: "A tranquil village nestled in a lush valley, with small, cozy houses dotting the landscape, surrounded by towering, snow-capped mountains under a clear blue sky. A gentle river meanders through the village, reflecting the warm glow of the sunrise"

casino Text prompt: "An ancient library buried beneath the earth, its halls lit by glowing crystals, with scrolls and tomes stacked in endless rows"

casino Text prompt: "A bustling spaceport on a distant planet, with ships of various designs taking off against a backdrop of twin moons"

casino Text prompt: "A set of ancient armor, standing as if worn by an invisible warrior, in front of a backdrop of medieval banners and weaponry."

casino Text prompt: "An elephant painting a colorful abstract masterpiece with its trunk, in a studio surrounded by amused onlookers."

casino Text prompt: "Tiger in construction gear, perched on aged wooden docks, formidable, curious, tiger on the waterfront, textured, vibrant, atmospheric, sharp focus, lifelike, professional lighting, cinematic, 8K"

casino Text prompt: "Cluttered house in the woods, anime, oil painting, high resolution, cottagecore, ghibli inspired, 4k"

casino Text prompt: "An old, creepy dollhouse in a dusty attic, with dolls posed in unsettling positions. Cobwebs, dim lighting, and the shadows of unseen presences create a chilling scene"

lock Text prompt: "A stunning, intricately detailed painting of a sunset in a forest valley, blending the rich, symmetrical styles of Dan Mumford and Marc Simonetti with astrophotography elements"

lock Text prompt: "Create a photorealistic scene of a powerful storm with swirling, dark clouds and fierce winds approaching a coastal village. Show villagers preparing for the storm, with detailed architecture reflecting a fantasy world"

lock Text prompt: "Cyberpunk cityscape with towering skyscrapers, neon signs, and flying cars"

Stable Video Diffusion

We also studied the effect of optimized schedules in video generation using the open-source image-to-video model Stable Video Diffusion. We find that using optimized schedules leads to more stable videos with less color distortions as the video progresses. Below we show side-by-side comparisons of videos generated with 10 DDIM steps using the two different schedules.

Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis

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Researchers detect a new molecule in space

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New research from the group of MIT Professor Brett McGuire has revealed the presence of a previously unknown molecule in space. The team's open-access paper, “ Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I ,” appears in April 12 issue of The Astrophysical Journal Letters .

Zachary T.P. Fried , a graduate student in the McGuire group and the lead author of the publication, worked to assemble a puzzle comprised of pieces collected from across the globe, extending beyond MIT to France, Florida, Virginia, and Copenhagen, to achieve this exciting discovery. 

“Our group tries to understand what molecules are present in regions of space where stars and solar systems will eventually take shape,” explains Fried. “This allows us to piece together how chemistry evolves alongside the process of star and planet formation. We do this by looking at the rotational spectra of molecules, the unique patterns of light they give off as they tumble end-over-end in space. These patterns are fingerprints (barcodes) for molecules. To detect new molecules in space, we first must have an idea of what molecule we want to look for, then we can record its spectrum in the lab here on Earth, and then finally we look for that spectrum in space using telescopes.”

Searching for molecules in space

The McGuire Group has recently begun to utilize machine learning to suggest good target molecules to search for. In 2023, one of these machine learning models suggested the researchers target a molecule known as 2-methoxyethanol. 

“There are a number of 'methoxy' molecules in space, like dimethyl ether, methoxymethanol, ethyl methyl ether, and methyl formate, but 2-methoxyethanol would be the largest and most complex ever seen,” says Fried. To detect this molecule using radiotelescope observations, the group first needed to measure and analyze its rotational spectrum on Earth. The researchers combined experiments from the University of Lille (Lille, France), the New College of Florida (Sarasota, Florida), and the McGuire lab at MIT to measure this spectrum over a broadband region of frequencies ranging from the microwave to sub-millimeter wave regimes (approximately 8 to 500 gigahertz). 

The data gleaned from these measurements permitted a search for the molecule using Atacama Large Millimeter/submillimeter Array (ALMA) observations toward two separate star-forming regions: NGC 6334I and IRAS 16293-2422B. Members of the McGuire group analyzed these telescope observations alongside researchers at the National Radio Astronomy Observatory (Charlottesville, Virginia) and the University of Copenhagen, Denmark. 

“Ultimately, we observed 25 rotational lines of 2-methoxyethanol that lined up with the molecular signal observed toward NGC 6334I (the barcode matched!), thus resulting in a secure detection of 2-methoxyethanol in this source,” says Fried. “This allowed us to then derive physical parameters of the molecule toward NGC 6334I, such as its abundance and excitation temperature. It also enabled an investigation of the possible chemical formation pathways from known interstellar precursors.”

Looking forward

Molecular discoveries like this one help the researchers to better understand the development of molecular complexity in space during the star formation process. 2-methoxyethanol, which contains 13 atoms, is quite large for interstellar standards — as of 2021, only six species larger than 13 atoms were detected outside the solar system , many by McGuire’s group, and all of them existing as ringed structures.  

“Continued observations of large molecules and subsequent derivations of their abundances allows us to advance our knowledge of how efficiently large molecules can form and by which specific reactions they may be produced,” says Fried. “Additionally, since we detected this molecule in NGC 6334I but not in IRAS 16293-2422B, we were presented with a unique opportunity to look into how the differing physical conditions of these two sources may be affecting the chemistry that can occur.”

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Announcing the NeurIPS 2023 Paper Awards 

Communications Chairs 2023 2023 Conference awards , neurips2023

By Amir Globerson, Kate Saenko, Moritz Hardt, Sergey Levine and Comms Chair, Sahra Ghalebikesabi 

We are honored to announce the award-winning papers for NeurIPS 2023! This year’s prestigious awards consist of the Test of Time Award plus two Outstanding Paper Awards in each of these three categories: 

• Two Outstanding Main Track Papers 
• Two Outstanding Main Track Runner-Ups 
• Two Outstanding Datasets and Benchmark Track Papers  

This year’s organizers received a record number of paper submissions. Of the 13,300 submitted papers that were reviewed by 968 Area Chairs, 98 senior area chairs, and 396 Ethics reviewers 3,540  were accepted after 502 papers were flagged for ethics reviews . 

We thank the awards committee for the main track: Yoav Artzi, Chelsea Finn, Ludwig Schmidt, Ricardo Silva, Isabel Valera, and Mengdi Wang. For the Datasets and Benchmarks track, we thank Sergio Escalera, Isabelle Guyon, Neil Lawrence, Dina Machuve, Olga Russakovsky, Hugo Jair Escalante, Deepti Ghadiyaram, and Serena Yeung. Conflicts of interest were taken into account in the decision process.

Congratulations to all the authors! See Posters Sessions Tue-Thur in Great Hall & B1-B2 (level 1).

Outstanding Main Track Papers

Privacy Auditing with One (1) Training Run Authors: Thomas Steinke · Milad Nasr · Matthew Jagielski

Poster session 2: Tue 12 Dec 5:15 p.m. — 7:15 p.m. CST, #1523

Oral: Tue 12 Dec 3:40 p.m. — 4:40 p.m. CST, Room R06-R09 (level 2)

Abstract: We propose a scheme for auditing differentially private machine learning systems with a single training run. This exploits the parallelism of being able to add or remove multiple training examples independently. We analyze this using the connection between differential privacy and statistical generalization, which avoids the cost of group privacy. Our auditing scheme requires minimal assumptions about the algorithm and can be applied in the black-box or white-box setting. We demonstrate the effectiveness of our framework by applying it to DP-SGD, where we can achieve meaningful empirical privacy lower bounds by training only one model. In contrast, standard methods would require training hundreds of models.

Are Emergent Abilities of Large Language Models a Mirage? Authors: Rylan Schaeffer · Brando Miranda · Sanmi Koyejo

Poster session 6: Thu 14 Dec 5:00 p.m. — 7:00 p.m. CST, #1108

Oral: Thu 14 Dec 3:20 p.m. — 3:35 p.m. CST, Hall C2 (level 1) 

Abstract: Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability , appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher’s choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous, predictable changes in model performance. We present our alternative explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities, (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show how to choose metrics to produce never-before-seen seemingly emergent abilities in multiple vision tasks across diverse deep networks. Via all three analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.

Outstanding Main Track Runner-Ups

Scaling Data-Constrained Language Models Authors : Niklas Muennighoff · Alexander Rush · Boaz Barak · Teven Le Scao · Nouamane Tazi · Aleksandra Piktus · Sampo Pyysalo · Thomas Wolf · Colin Raffel

Poster session 2: Tue 12 Dec 5:15 p.m. — 7:15 p.m. CST, #813

Oral: Tue 12 Dec 3:40 p.m. — 4:40 p.m. CST, Hall C2 (level 1)  

Abstract : The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations .

Direct Preference Optimization: Your Language Model is Secretly a Reward Model Authors: Rafael Rafailov · Archit Sharma · Eric Mitchell · Christopher D Manning · Stefano Ermon · Chelsea Finn

Poster session 6: Thu 14 Dec 5:00 p.m. — 7:00 p.m. CST, #625

Oral: Thu 14 Dec 3:50 p.m. — 4:05 p.m. CST, Ballroom A-C (level 2)  

Abstract: While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF). However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model. In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized exactly with a single stage of policy training, essentially solving a classification problem on the human preference data. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning. Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO exceeds RLHF’s ability to control sentiment of generations and improves response quality in summarization and single-turn dialogue while being substantially simpler to implement and train.

Outstanding Datasets and Benchmarks Papers

In the dataset category : 

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Authors:  Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Julius Busecke · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Tian Zheng · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Mike Pritchard

Poster session 4: Wed 13 Dec 5:00 p.m. — 7:00 p.m. CST, #105 

Oral: Wed 13 Dec 3:45 p.m. — 4:00 p.m. CST, Ballroom A-C (level 2)

Abstract: Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.   

In the benchmark category :

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Authors: Boxin Wang · Weixin Chen · Hengzhi Pei · Chulin Xie · Mintong Kang · Chenhui Zhang · Chejian Xu · Zidi Xiong · Ritik Dutta · Rylan Schaeffer · Sang Truong · Simran Arora · Mantas Mazeika · Dan Hendrycks · Zinan Lin · Yu Cheng · Sanmi Koyejo · Dawn Song · Bo Li

Poster session 1: Tue 12 Dec 10:45 a.m. — 12:45 p.m. CST, #1618  

Oral: Tue 12 Dec 10:30 a.m. — 10:45 a.m. CST, Ballroom A-C (Level 2)

Abstract: Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications to healthcare and finance – where mistakes can be costly. To this end, this work proposes a comprehensive trustworthiness evaluation for large language models with a focus on GPT-4 and GPT-3.5, considering diverse perspectives – including toxicity, stereotype bias, adversarial robustness, out-of-distribution robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. Based on our evaluations, we discover previously unpublished vulnerabilities to trustworthiness threats. For instance, we find that GPT models can be easily misled to generate toxic and biased outputs and leak private information in both training data and conversation history. We also find that although GPT-4 is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more vulnerable given jailbreaking system or user prompts, potentially due to the reason that GPT-4 follows the (misleading) instructions more precisely. Our work illustrates a comprehensive trustworthiness evaluation of GPT models and sheds light on the trustworthiness gaps. Our benchmark is publicly available at https://decodingtrust.github.io/.

Test of Time

This year, following the usual practice, we chose a NeurIPS paper from 10 years ago to receive the Test of Time Award, and “ Distributed Representations of Words and Phrases and their Compositionality ” by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, won. 

Published at NeurIPS 2013 and cited over 40,000 times, the work introduced the seminal word embedding technique word2vec. Demonstrating the power of learning from large amounts of unstructured text, the work catalyzed progress that marked the beginning of a new era in natural language processing.

Greg Corrado and Jeffrey Dean will be giving a talk about this work and related research on Tuesday, 12 Dec at 3:05 – 3:25 pm CST in Hall F.  

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Black workers’ views and experiences in the U.S. labor force stand out in key ways

There are more than 21 million Black Americans in the U.S. labor force today. Their workforce experiences are varied but stand out from people of other races and ethnicities on several important measures: They are more likely to be employed in certain postal work, transit, health care and security fields; report experiencing more racial discrimination on the job; and place a higher value on diversity, equity and inclusion efforts in the workplace.

For Labor Day, here are facts about Black workers’ labor force experiences and attitudes, drawn from federal data sources and recent Pew Research Center surveys.

Pew Research Center conducted this analysis to understand the views and experiences of Black workers in the United States and how they differ from those of people from other racial or ethnic backgrounds. Findings are based on data from the U.S. Bureau of Labor Statistics, the U.S. Census Bureau and Center surveys. Additional information about each survey and its methodology can be found in the links in the text of this post.

In the Center surveys, references to workers or employed adults include those who are employed part time or full time; are not self-employed; have only one job or have multiple jobs but consider one their primary job; and whose company or organization has 10 or more people.

References to White, Black and Asian adults include those who are not Hispanic and identify as only one race. Hispanics are of any race. Asian American respondents include only English speakers.  

Black Americans make up large shares of workers in certain transit, health and security occupations, according to Bureau of Labor Statistics (BLS) data from 2022, the most recent year available. Black workers account for about 13% of all U.S. workers, including those who work full time, part time and are self-employed. They make up especially large shares of employees in certain occupations, including postal service clerks (40.4%), transit and intercity bus drivers (36.6%), nursing assistants (36.0%), security guards and gambling surveillance officers (34.5%), and home health aides (32.5%).

Black workers make up much smaller shares of farmers, ranchers and other agricultural managers (1.5%). They also tend to be underrepresented in some science, engineering and technology occupations such as veterinarians (2.2%), mechanical engineers (3.6%) and electrical and electronics engineers (6.0%).

A 2021 Center survey found that Black adults see barriers for Black workers in STEM fields, including an unwelcoming professional environment and the need for more mentorship and representation for young people in science, technology, engineering and math.

Black workers generally earn less than U.S. workers overall, according to BLS data from 2022. Among full-time wage and salary workers, the median weekly earnings for Black workers ages 16 and older are $878, compared with $1,059 for all U.S. workers in the same age group. Among workers of other races and ethnicities in the same age group, the median weekly earnings are $823 for Hispanic workers, $1,085 for White workers and $1,401 for Asian workers. And the differences hold when accounting for education level – Black workers earn less than those in other groups even among workers with bachelor’s or advanced degrees.

Household income for Black Americans has lagged behind that for Americans of other races for several decades, according to U.S. Census Bureau data .

The unemployment rate for Black Americans is the highest of any racial or ethnic group and roughly double the rate for the U.S. overall, BLS data shows. In 2022, the unemployment rate for Americans ages 16 and older was 3.7% for men and 3.6% for women, according to BLS annual averages . Among Black Americans, the unemployment rate was 6.3% for men and 6.0% for women. This compared with around 3% each for White and Asian men and women and about 4% each for Hispanic men and women.

Monthly unemployment figures showed a record-low unemployment rate for Black Americans in April of this year, but it has begun to tick back up .

As with gaps in household income, Black Americans have experienced higher unemployment rates than their White counterparts for decades. Researchers have identified a variety of factors causing this trend , including racial discrimination and gaps in education, skills and work experience.

Black workers are the most likely to say they’ve experienced discrimination at work because of their race or ethnicity, according to a February 2023 Center survey of U.S. workers . About four-in-ten Black workers (41%) say they have experienced discrimination or been treated unfairly by an employer in hiring, pay or promotions because of their race or ethnicity. Much smaller shares of Asian (25%), Hispanic (20%) and White (8%) workers say the same.

Among Black workers, 48% of men and 36% of women say they’ve experienced discrimination or unfair treatment by an employer due to their race. There are no gender differences among White and Hispanic workers, and the sample size for Asian workers is too small to analyze men and women separately.

A quarter of U.S. workers say being Black makes it harder to succeed where they work, the February survey shows. Just 8% of U.S. workers say being Black makes it a little or a lot easier to be successful where they work, 50% say it makes it neither easier nor harder, and 17% aren’t sure.

Among Black workers, 51% say that being Black makes it harder to succeed where they work. By comparison, 41% of Asian, 23% of Hispanic and 18% of White workers view being Black as a disadvantage in their workplace. And about four-in-ten or fewer among Asian (39%), Hispanic (29%) and White (7%) workers say that being their own race or ethnicity makes it harder to be successful where they work.

Majorities of Black Americans see racial and ethnic bias as a major problem in hiring and performance evaluations generally, according to a separate Center survey of all U.S. adults conducted in December 2022 . Some 64% of Black adults say that, in hiring generally, bias and unfair treatment based on job applicants’ race or ethnicity is a major problem. This compares with 49% of Asian, 41% of Hispanic and 30% of White adults who view racial and ethnic bias in hiring as a major problem.

When it comes to performance evaluations, 56% of Black adults say that, in general, racial and ethnic bias is a major problem. About four-in-ten Asian or Hispanic adults and 23% of White adults say the same.

Black workers especially value diversity in their workplace, the February survey of workers found. Regardless of how diverse their workplace is, 53% of Black workers say it is extremely or very important to them to work somewhere with a mix of employees of different races and ethnicities. That percentage is larger than the shares of Hispanic, White and Asian workers who say this. And 42% of Black workers say they highly value a workplace with employees of different ages, compared with smaller shares of workers who are Hispanic (33%), Asian (30%) or White (24%).

There is a similar trend in views of workplace accessibility: 62% of Black workers say it is extremely or very important to them to work at a place that is accessible for people with physical disabilities, compared with 51% of Hispanic, 48% of White and 43% of Asian workers.

The vast majority of Black workers say that increasing diversity, equity and inclusion (DEI) at work is a good thing, but a sizable share give their employer low marks in this area, according to the February workers survey .

Around eight-in-ten Black workers (78%) say that focusing on increasing DEI at work is a good thing. Just 1% of Black workers say this is a bad thing, and 20% view it as neither good nor bad. While majorities of Asian (72%) and Hispanic (65%) workers also say that focusing on increasing DEI is a good thing, roughly half (47%) of White workers hold this view. In fact, 21% of White workers say it’s a bad thing.

But when it comes to their own employer’s DEI efforts, 28% of Black workers say their company or organization pays too little attention to increasing DEI – the largest share of any racial or ethnic group. Black workers are also the least likely to say that their company or organization pays too much attention to DEI. Just 3% hold this view, compared with one-in-ten or more among Hispanic (11%), White (16%) and Asian (18%) workers.

• Black Americans
• Business & Workplace
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• Racial Bias & Discrimination

Katherine Schaeffer is a research analyst at Pew Research Center

A look at Black-owned businesses in the U.S.

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