{"ID":2862027,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00777","arxiv_id":"2510.00777","title":"In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration","abstract":"LLM-generated drafts often contain subtle factual or logical errors, yet prior work shows that models struggle to reliably integrate multi-turn feedback aimed at fixing them. We propose in-place feedback, an interaction paradigm in which the user directly edits the model's previous response and the model continues generation from the edited context. In-place feedback consistently outperforms standard multi-turn feedback across five reasoning-intensive benchmarks while requiring fewer tokens, and our fine-grained analysis shows that it applies corrections more reliably and propagates them to subsequent reasoning. A user study with domain experts refining LLM-generated summaries corroborates these findings: participants report higher final-output satisfaction and substantially lower fatigue with in-place feedback, and a mixed strategy combining in-place and multi-turn feedback scores highest on every measured dimension. These results suggest that editing errors directly is a more effective paradigm for expert-LLM collaboration.","short_abstract":"LLM-generated drafts often contain subtle factual or logical errors, yet prior work shows that models struggle to reliably integrate multi-turn feedback aimed at fixing them. We propose in-place feedback, an interaction paradigm in which the user directly edits the model's previous response and the model continues gene...","url_abs":"https://arxiv.org/abs/2510.00777","url_pdf":"https://arxiv.org/pdf/2510.00777v2","authors":"[\"Youngbin Choi\",\"Minjong Lee\",\"Saemi Moon\",\"Seunghyuk Cho\",\"Chaehyeon Chung\",\"MoonJeong Park\",\"Dongwoo Kim\"]","published":"2025-10-01T11:16:04Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Large Language Model\"]","has_code":false}