@inproceedings{michael-etal-2023-nlp,
    title = "What Do {NLP} Researchers Believe? Results of the {NLP} Community Metasurvey",
    author = "Michael, Julian  and
      Holtzman, Ari  and
      Parrish, Alicia  and
      Mueller, Aaron  and
      Wang, Alex  and
      Chen, Angelica  and
      Madaan, Divyam  and
      Nangia, Nikita  and
      Pang, Richard Yuanzhe  and
      Phang, Jason  and
      Bowman, Samuel R.",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.903",
    doi = "10.18653/v1/2023.acl-long.903",
    pages = "16334--16368",
    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.",
}

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.
Michael, Julian Holtzman, Ari Parrish, Alicia Mueller, Aaron Wang, Alex Chen, Angelica Madaan, Divyam Nangia, Nikita Pang, Richard Yuanzhe Phang, Jason Bowman, Samuel R.
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey