发表机构
University College Dublin（都柏林大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出可变转录范式，允许用户通过自然语言编辑对话历史，以缓解上下文污染，实验表明其能提升交互质量并减少重启意图。
AI 中文摘要
当代大型语言模型（LLM）聊天系统将对话历史视为不可变的轮次序列，该序列定义了模型的工作上下文。然而，真实交互中的用户意图并非静态不变：它通过纠正、细化和不断变化的约束而演变。动态意图与静态转录之间的这种不匹配可能导致上下文污染，即过时或无关的信息持续存在并继续影响后续响应。我们引入了可变转录（mutable transcripts），这是一种新的交互范式，使用户能够通过自然语言编辑请求修改先前的轮次，从而允许对话历史本身被更新而非追加。这将转录从被动记录重新定义为对话状态的可编辑表示。我们展示了一个工作原型，将转录级修订集成到标准聊天界面中，并通过受控用户研究（n=17）和代表性交互场景的说明性转录分析来评估其可行性。参与者在清晰度、信心和易用性方面显著偏好可变转录而非标准聊天，并且重启对话的意图有所降低。对代表性用户研究对话的转录分析表明，可变转录可以减少对话长度并消除过时的保留上下文。这些发现提供了初步证据，表明用户驱动的对话历史修订可以提高交互质量，并有助于维持用户意图的更当前表示。源代码和原型可通过此 https URL 访问。
英文摘要
Contemporary large language model (LLM) chat systems treat conversation history as an immutable sequence of turns that defines the model's working context. However, user intent in real interactions is not static: it evolves through correction, refinement, and shifting constraints. This mismatch between dynamic intent and static transcripts can result in context pollution, where outdated or irrelevant information persists and continues to influence subsequent responses. We introduce mutable transcripts, a new interaction paradigm that enables users to revise prior turns through natural language edit requests, allowing the conversation history itself to be updated rather than appended. This reframes the transcript from a passive record into an editable representation of conversational state. We present a working prototype that integrates transcript-level revision into a standard chat interface and evaluate its feasibility through a controlled user study (n=17) and an illustrative transcript analysis of representative interaction scenarios. Participants significantly preferred mutable transcripts over standard chat across measures of clarity, confidence, and ease of use, with reduced intent to restart conversations. Transcript analysis of representative user study conversations shows that mutable transcripts can reduce conversation length and eliminate obsolete retained context. These findings provide initial evidence that user-driven revision of conversational history can improve interaction quality and help maintain a more current representation of user intent. The source code and prototype can be accessed at https://github.com/QxLabIreland/ReChat
CommentsAccepted at NeurIPS 2026
