@inproceedings{choi-etal-2026-place,
title = "In-Place Feedback: Reliable Refinement for Multi-Turn Expert-{LLM} Collaboration",
author = "Choi, Youngbin and
Lee, Minjong and
Moon, Saemi and
Cho, Seunghyuk and
Chung, Chaehyeon and
Park, MoonJeong and
Kim, Dongwoo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1338/",
pages = "30503--30545",
ISBN = "979-8-89176-459-0",
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 improves every dimension we measure. These results suggest that editing errors in place is a more reliable way to incorporate expert corrections than multi-turn feedback, while combining the two acts complementarily to form a more effective collaboration than either mechanism alone."
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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 improves every dimension we measure. These results suggest that editing errors in place is a more reliable way to incorporate expert corrections than multi-turn feedback, while combining the two acts complementarily to form a more effective collaboration than either mechanism alone.</abstract>
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%0 Conference Proceedings
%T In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration
%A Choi, Youngbin
%A Lee, Minjong
%A Moon, Saemi
%A Cho, Seunghyuk
%A Chung, Chaehyeon
%A Park, MoonJeong
%A Kim, Dongwoo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F choi-etal-2026-place
%X 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 improves every dimension we measure. These results suggest that editing errors in place is a more reliable way to incorporate expert corrections than multi-turn feedback, while combining the two acts complementarily to form a more effective collaboration than either mechanism alone.
%U https://aclanthology.org/2026.emnlp-main.1338/
%P 30503-30545
Markdown (Informal)
[In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration](https://aclanthology.org/2026.emnlp-main.1338/) (Choi et al., EMNLP 2026)
ACL