Generating Edit-Inducing Questions for AI Research Manuscripts

arXiv:2609.36617 · cs.CL · Submitted 2026-09-29 · Read on arXiv

cs.CL

Submitted: 2026-09-29

Updated: 2026-10-07

Terminology

• Aggregation — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 9 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Paper Review — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• PeerQA: A Scientific Question Answering Dataset from Peer Reviews
• AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot
• MARG: Multi-Agent Review Generation for Scientific Papers
• A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
• Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards
• Can large language models provide useful feedback on research papers? A large-scale empirical analysis
• ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing
• A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences

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