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arXiv · 2609.36617

Generating Edit-Inducing Questions for AI Research Manuscripts

Sebastian Joseph·Zichao Wang·Jennifer Healey·Alexa Siu·Junyi Jessy Li·Ani Nenkova
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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.

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Sebastian Joseph, Zichao Wang, Jennifer Healey, Alexa Siu, Junyi Jessy Li, Ani Nenkova. 2026-09-29. Generating Edit-Inducing Questions for AI Research Manuscripts. https://arxiv.org/abs/2609.36617

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