AI RESEARCH
In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration
arXiv CS.LG
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ArXi:2510.00777v2 Announce Type: replace 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.