What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
Not on the map. Why?
Abstract (arXiv)
We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field, concerns about AGI, and ethics. Our results put concrete numbers to several controversies: For example, respondents are split almost exactly in half on questions about the importance of artificial general intelligence, whether language models understand language, and the necessity of linguistic structure and inductive bias for solving NLP problems. In addition, the survey posed meta-questions, asking respondents to predict the distribution of survey responses. This allows us not only to gain insight on the spectrum of beliefs held by NLP researchers, but also to uncover false sociological beliefs where the community's predictions don't match reality. We find such mismatches on a wide range of issues. Among other results, the community greatly overestimates its own belief in the usefulness of benchmarks and the potential for scaling to solve real-world problems, while underestimating its own belief in the importance of linguistic structure, inductive bias, and interdisciplinary science.
Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 19 of its 65 references that are in the map. First posted on arXiv 26 Aug 2022.
Builds on
19 of 65 references.
References · 19
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2016Concrete Problems in AI Safetycited by 1,582
- 2019Attention is not Explanationcited by 916
- 2019Attention is not not Explanationcited by 629
- 2019Green AIcited by 620
- 2021AI and the Everything in the Whole Wide World Benchmarkcited by 340
- 2019Risks from Learned Optimization in Advanced Machine Learning Systemscited by 250
- 2021What Will it Take to Fix Benchmarking in Natural Language Understanding?cited by 200
- 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
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- 2018Pathologies of Neural Models Make Interpretations Difficultcited by 140
- 2022Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?cited by 103
- 2021Do Transformer Modifications Transfer Across Implementations and Applications?cited by 87
- 2021Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?cited by 55
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Citing papers per year: 2022: 3; 2023: 13; 2024: 4; 2025: 7; 2026: 1.
28 papers.
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- 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
- 2023Model evaluation for extreme riskscited by 167
- 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
- 2023Eight Things to Know about Large Language Modelscited by 73
- 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
- 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluationcited by 57
- 2024Benchmarks as Microscopes: A Call for Model Metrologycited by 33
- 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performancecited by 25
- 2023Creating a Large Language Model of a Philosophercited by 20
- 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fieldscited by 20
- 2023Reproducibility in NLP: What Have We Learned from the Checklist?cited by 18
- 2023On pitfalls (and advantages) of sophisticated large language modelscited by 7
16 more
- 2025Critical Foreign Policy Decisions (CFPD)-Benchmark: Measuring Diplomatic Preferences in Large Language Modelscited by 7
- 2023On the Origins of Bias in NLP through the Lens of the Jim Codecited by 5
- 2023The Two Word Test: A Semantic Benchmark for Large Language Modelscited by 5
- 2023To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processingcited by 4
- 2024On Tables with Numbers, with Numberscited by 4
- 2022Language Models Understand Us, Poorlycited by 3
- 2023Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processingcited by 3
- 2025Research Borderlands: Analysing Writing Across Research Culturescited by 3
- 2025NeurIPS should lead scientific consensus on AI policycited by 3
- 2023Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributionscited by 2
- 2024What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Modelscited by 2
- 2025Reassessing Active Learning Adoption in Contemporary NLP: A Community Surveycited by 2
- 2025Research Community Perspectives on "Intelligence" and Large Language Modelscited by 2
- 2024Can Large Language Models Create New Knowledge for Spatial Reasoning Tasks?cited by 1
- 2025Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?cited by 1
- 2026The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processingcited by 0
- 2026The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing2 Jul 2026
- 2025Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?6 Oct 2025
- 2025NeurIPS should lead scientific consensus on AI policy30 Sep 2025
- 2025Research Borderlands: Analysing Writing Across Research Cultures1 Jun 2025
- 2025Research Community Perspectives on "Intelligence" and Large Language Models27 May 2025
- 2025Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey12 Mar 2025
- 2025Critical Foreign Policy Decisions (CFPD)-Benchmark: Measuring Diplomatic Preferences in Large Language Models8 Mar 2025
- 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation10 Feb 2025
- 2024On Tables with Numbers, with Numbers12 Aug 2024
- 2024Benchmarks as Microscopes: A Call for Model Metrology22 Jul 2024
- 2024What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models2 Jul 2024
- 2024Can Large Language Models Create New Knowledge for Spatial Reasoning Tasks?23 May 2024
16 more
- 2023Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions6 Dec 2023
- 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields23 Oct 2023
- 2023To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing11 Oct 2023
- 2023Reproducibility in NLP: What Have We Learned from the Checklist?16 Jun 2023
- 2023The Two Word Test: A Semantic Benchmark for Large Language Models7 Jun 2023
- 2023Model evaluation for extreme risks24 May 2023
- 2023On the Origins of Bias in NLP through the Lens of the Jim Code16 May 2023
- 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance15 May 2023
- 2023Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing17 Apr 2023
- 2023Eight Things to Know about Large Language Models2 Apr 2023
- 2023On pitfalls (and advantages) of sophisticated large language models25 Feb 2023
- 2023The Gradient of Generative AI Release: Methods and Considerations5 Feb 2023
- 2023Creating a Large Language Model of a Philosopher2 Feb 2023
- 2022Discovering Language Model Behaviors with Model-Written Evaluations19 Dec 2022
- 2022Language Models Understand Us, Poorly19 Oct 2022
- 2022The Debate Over Understanding in AI's Large Language Models14 Oct 2022
- 2023Model evaluation for extreme riskscited by 167
- 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
- 2023Eight Things to Know about Large Language Modelscited by 73
- 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
- 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluationcited by 57
- 2024Benchmarks as Microscopes: A Call for Model Metrologycited by 33
- 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performancecited by 25
- 2023Creating a Large Language Model of a Philosophercited by 20
- 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fieldscited by 20
- 2023Reproducibility in NLP: What Have We Learned from the Checklist?cited by 18
- 2023On pitfalls (and advantages) of sophisticated large language modelscited by 7
- 2025Critical Foreign Policy Decisions (CFPD)-Benchmark: Measuring Diplomatic Preferences in Large Language Modelscited by 7
12 most cited of 26 All 26 (JSON)
- 2026The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing2 Jul 2026
- 2025Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?6 Oct 2025
- 2025NeurIPS should lead scientific consensus on AI policy30 Sep 2025
- 2025Research Borderlands: Analysing Writing Across Research Cultures1 Jun 2025
- 2025Research Community Perspectives on "Intelligence" and Large Language Models27 May 2025
- 2025Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey12 Mar 2025
- 2025Critical Foreign Policy Decisions (CFPD)-Benchmark: Measuring Diplomatic Preferences in Large Language Models8 Mar 2025
- 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation10 Feb 2025
- 2024On Tables with Numbers, with Numbers12 Aug 2024
- 2024Benchmarks as Microscopes: A Call for Model Metrology22 Jul 2024
- 2024What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models2 Jul 2024
- 2023Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions6 Dec 2023
12 newest of 26 All 26 (JSON)