野外LLM辅助写作中原型人机协作行为
Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild
AI总结：
本研究通过分析Bing Copilot和WildChat中的大规模写作交互，识别出少数原型人机协作行为（PATHs）可解释用户与LLM交互的主要变异，并发现写作意图与PATHs显著相关，为LLM对齐提供启示。
摘要：
随着大型语言模型（LLMs）被用于复杂的写作流程中，用户通过多轮交互来引导生成结果以更好地满足其需求。用户并非被动接受输出，而是主动地细化、探索和共同构建文本。我们对使用两个流行AI助手（Bing Copilot和WildChat）在野外进行写作任务的用户的这种协作行为进行了大规模分析。我们的分析超越了先前工作中常见的简单任务分类或满意度估计，而是刻画了用户在整个会话过程中如何与LLM交互。我们识别出用户在其初始请求之后的提示中与LLM交互的原型行为。我们将这些称为原型人机协作行为（PATHs），并发现一小部分PATHs解释了用户与LLM交互中观察到的大部分变异。这些PATHs涵盖用户修订意图、探索文本、提出问题、调整风格或注入新内容。接下来，我们发现了特定写作意图与PATHs之间的统计显著相关性，揭示了用户的意图如何塑造其协作行为。最后，我们讨论了我们的发现对LLM对齐的启示。
英文摘要：
As large language models (LLMs) are used in complex writing workflows, users engage in multi-turn interactions to steer generations to better fit their needs. Rather than passively accepting output, users actively refine, explore, and co-construct text. We conduct a large-scale analysis of this collaborative behavior for users engaged in writing tasks in the wild with two popular AI assistants, Bing Copilot and WildChat. Our analysis goes beyond simple task classification or satisfaction estimation common in prior work and instead characterizes how users interact with LLMs through the course of a session. We identify prototypical behaviors in how users interact with LLMs in prompts following their original request. We refer to these as Prototypical Human-AI Collaboration Behaviors (PATHs) and find that a small group of PATHs explain a majority of the variation seen in user-LLM interaction. These PATHs span users revising intents, exploring texts, posing questions, adjusting style or injecting new content. Next, we find statistically significant correlations between specific writing intents and PATHs, revealing how users' intents shape their collaboration behaviors. We conclude by discussing the implications of our findings on LLM alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
