@inproceedings{joseph-etal-2026-generating,
title = "Generating Edit-Inducing Questions for {AI} Research Manuscripts",
author = "Joseph, Sebastian Antony and
Wang, Zichao and
Healey, Jennifer and
Siu, Alexa and
Li, Junyi Jessy and
Nenkova, Ani",
editor = "T.Y.S.S., Santosh and
Agarwal, Amit and
Gupta, Vivek and
Kementchedjhieva, Yova and
Garimella, Aparna and
Hou, Yufang",
booktitle = "The Proceedings of Workshop on Document Intelligence and Understanding",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.docinsights-1.29/",
pages = "428--439",
ISBN = "979-8-89176-477-4",
abstract = "We study the ability of LLMs to generate edit-inducing questions whose answer will improve a paper draft. On a dataset of paired submission and camera-ready papers from ICLR and NeurIPS, we compare the helpfulness of questions from GPT models with or without full paper context to that of human reviewers. GPT produces more edit-inducing questions and its questions are associated with more extensive edits and cover a broader range of edited content compared to questions from reviewers. However, a much smaller percentage of the GPT questions are edit-inducing. Our analyses confirm that automated questions can be beneficial to authors and highlight an example task where proper attending to long context deteriorates reasoning model ability to produce helpful output."
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<abstract>We study the ability of LLMs to generate edit-inducing questions whose answer will improve a paper draft. On a dataset of paired submission and camera-ready papers from ICLR and NeurIPS, we compare the helpfulness of questions from GPT models with or without full paper context to that of human reviewers. GPT produces more edit-inducing questions and its questions are associated with more extensive edits and cover a broader range of edited content compared to questions from reviewers. However, a much smaller percentage of the GPT questions are edit-inducing. Our analyses confirm that automated questions can be beneficial to authors and highlight an example task where proper attending to long context deteriorates reasoning model ability to produce helpful output.</abstract>
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%0 Conference Proceedings
%T Generating Edit-Inducing Questions for AI Research Manuscripts
%A Joseph, Sebastian Antony
%A Wang, Zichao
%A Healey, Jennifer
%A Siu, Alexa
%A Li, Junyi Jessy
%A Nenkova, Ani
%Y T.Y.S.S., Santosh
%Y Agarwal, Amit
%Y Gupta, Vivek
%Y Kementchedjhieva, Yova
%Y Garimella, Aparna
%Y Hou, Yufang
%S The Proceedings of Workshop on Document Intelligence and Understanding
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-477-4
%F joseph-etal-2026-generating
%X We study the ability of LLMs to generate edit-inducing questions whose answer will improve a paper draft. On a dataset of paired submission and camera-ready papers from ICLR and NeurIPS, we compare the helpfulness of questions from GPT models with or without full paper context to that of human reviewers. GPT produces more edit-inducing questions and its questions are associated with more extensive edits and cover a broader range of edited content compared to questions from reviewers. However, a much smaller percentage of the GPT questions are edit-inducing. Our analyses confirm that automated questions can be beneficial to authors and highlight an example task where proper attending to long context deteriorates reasoning model ability to produce helpful output.
%U https://aclanthology.org/2026.docinsights-1.29/
%P 428-439
Markdown (Informal)
[Generating Edit-Inducing Questions for AI Research Manuscripts](https://aclanthology.org/2026.docinsights-1.29/) (Joseph et al., DocInsights 2026)
ACL
- Sebastian Antony Joseph, Zichao Wang, Jennifer Healey, Alexa Siu, Junyi Jessy Li, and Ani Nenkova. 2026. Generating Edit-Inducing Questions for AI Research Manuscripts. In The Proceedings of Workshop on Document Intelligence and Understanding, pages 428–439, Budapest, Hungary. Association for Computational Linguistics.