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Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment

Paper · 2024 · Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong et al.

Summary

Large conferences ask authors to fill in compliance checklists, but making sure those answers are accurate is hard to do at scale. This study tests whether an LLM can help with that narrow, low-stakes task. At NeurIPS 2024 the team offered an optional "Checklist Assistant." Authors uploaded a manuscript along with their answers and justifications for the 15-item NeurIPS Paper Checklist, which covers claims, limitations, reproducibility, compute, ethics, licenses, IRB and similar topics. The LLM then returned a critique for each question. Only the authors saw this output; reviewers and chairs never had access to it. The evaluation combined surveys before and after use, an analysis of the generated feedback, a comparison of papers uploaded twice, and an adversarial manipulation test. Most participants considered the tool helpful and planned revisions, although their opinion after use was lower than their expectations beforehand. Complaints focused on incorrect or overly demanding feedback. Rewritten justifications could also push the assistant toward leniency. The work provides real-world evidence that LLMs can support guideline compliance, and it shows that human oversight is still required.

Key points

  • Participation: 539 pre-usage survey responses, 234 papers submitted to the assistant, and 78 post-usage survey responses.
  • Over 70% of post-use respondents rated the assistant useful, and about 70% intended to change their paper or checklist answers.
  • The most frequent complaints were inaccuracy (20 of 52 respondents) and excessive strictness (14 of 52).
  • The LLM typically gave 4–6 distinct points per checklist question. 35 respondents named concrete changes they would make. In the 40 papers checked twice (80 submissions), justifications grew noticeably longer on the second upload.
  • AI-assisted rewriting of justifications, with no real changes to the paper, made the assistant noticeably more lenient. Code, prompts and test papers are on GitHub (ihsaan-ullah/neurips-checklist-assistant).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Carnegie Mellon UniversityPittsburgh · United States539 works · 179,533 citations🎓University of ChicagoChicago · United States45 works · 50,617 citations🎓Université Paris-SaclayGif-sur-Yvette · France45 works · 6,127 citations🔬Laboratoire Interdisciplinaire des Sciences du NumériqueGif-sur-Yvette · France14 works · 507 citations

Also on the byline: ChaLearn · TAU: IA : vers des problèmes mieux posés.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksDecision Sciences · 209 worksInformation Systems and Management · 41 worksHistory and Philosophy of Science · 53 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, Benedictus Kent Rachmat, Zhen Xu, Isabelle Guyon and Nihar B. Shah. 2024. Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment. arXiv:2411.03417.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2411.03417 · arXiv 2411.03417

@misc{goldberg2024usefulness,
  title = {{Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment}},
  author = {Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, Benedictus Kent Rachmat, Zhen Xu, Isabelle Guyon and Nihar B. Shah},
  year = {2024},
  doi = {10.48550/arxiv.2411.03417},
  eprint = {2411.03417},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2411.03417}
}

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