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arXiv · 2510.00777

In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration

Youngbin Choi·Minjong Lee·Saemi Moon·Seunghyuk Cho·Chaehyeon Chung·MoonJeong Park·Dongwoo Kim
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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.

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Youngbin Choi, Minjong Lee, Saemi Moon, Seunghyuk Cho, Chaehyeon Chung, MoonJeong Park, Dongwoo Kim. 2026-05-28. In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration. https://arxiv.org/abs/2510.00777

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