arXiv:2510.00777v2 Announce Type: replace 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
As LLMs become more integrated into complex workflows, the need for efficient and reliable human-AI collaboration on reasoning tasks becomes critical, driving research into improved feedback mechanisms.
This development proposes a more effective method for human correction of LLM outputs, which is crucial for deploying AI in high-stakes environments where accuracy and user trust are paramount.
The paradigm for human-LLM interaction shifts from simple multi-turn feedback to direct 'in-place' editing, promising more reliable refinement and efficient collaboration.
- · AI developers
- · LLM users
- · Software companies integrating AI
- · Industries requiring high-accuracy AI outputs
- · Less efficient multi-turn feedback systems
Refined LLM outputs lead to higher user satisfaction and broader adoption in professional settings.
Reduced cognitive load for users correcting AI, potentially accelerating workflow automation.
This improved human-AI collaboration could unlock new applications for AI that were previously too error-prone or hard to correct.
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Read at arXiv cs.LG