What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman
Abstract
We present the results of the NLP Community Metasurvey. Run from May to June 2022, it 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 in half on 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 to uncover false sociological beliefs where the community's predictions don't match reality. Among other results, we find that 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 belief in the importance of linguistic structure, inductive bias, and interdisciplinary science.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers5
- Toward Compositional Behavior in Neural Models: A Survey of Current ViewsKate McCurdy, Paul Soulos, Paul Smolensky, Roland Fernandez et al.EMNLP 2024 · 12 citations
- Pragmatic Norms Are All You Need - Why The Symbol Grounding Problem Does Not Apply to LLMsReto GubelmannEMNLP 2024 · 2 citations
- Research Borderlands: Analysing Writing Across Research CulturesShaily Bhatt, Tal August, Maria AntoniakACL 2025
- Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and ContributionsWill Aitken, Mohamed Abdalla, Karen Rudie, Catherine StinsonACL 2024
- Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?Grace LeFevre, Qingcheng Zeng, Adam Leif, Jason Jewell et al.EMNLP 2025
Builds on3
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- Climbing towards NLU: On Meaning, Form, and Understanding in the Age of DataEmily M. Bender, Alexander KollerACL 2020 · 914 citations
- Do Transformer Modifications Transfer Across Implementations and Applications?Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus et al.EMNLP 2021 · 80 citations
Related papers
- The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing ResearchMohamed Abdalla, Jan Philip Wahle, Terry Lima Ruas, Aurélie Névéol et al.ACL 2023 · 16 citations
- Mind in the Machine? Cross-Disciplinary Perceptions of Consciousness in Artificial IntelligenceHamid Moradi, Ignacio Avellino, Patrick Krauss, Dario Zanca et al.CHI 2026 · 1 citation
- A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better InterpretabilityXinyu Hu, Mingqi Gao, Li Lin, Zhenghan Yu et al.ACL 2025
- Defining Knowledge: Bridging Epistemology and Large Language ModelsConstanza Fierro, Ruchira Dhar, Filippos Stamatiou, Nicolas Garneau et al.EMNLP 2024 · 5 citations
- In Benchmarks We Trust ... Or Not?Ine Gevers, Victor De Marez, Jens Van Nooten, Jens Lemmens et al.EMNLP 2025 · 1 citation
