进入未知的未知：通过参与语言模型智能体对话实现参与式人类学习
Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations
AI总结：
针对用户在未知未知领域中难以探索信息的问题，本文提出 Co-STORM 框架，让用户观察并引导多个语言模型智能体对话以偶然发现新知识，并通过动态思维导图和综合报告辅助学习，在自动评估和人类评估中均优于搜索引擎与 RAG 聊天机器人。
摘要：
虽然由语言模型（LM）驱动的聊天机器人和生成式搜索引擎擅长回答具体查询，但在未知的未知领域中探索信息对用户而言仍然具有挑战性。为了模拟儿童/学生通过倾听并参与父母/老师对话来学习的常见教育场景，我们创建了 Collaborative STORM（Co-STORM）。与要求用户提出所有问题的问答系统不同，Co-STORM 允许用户观察并偶尔引导多个语言模型智能体之间的讨论。智能体代表用户提问，使用户能够偶然发现未知的未知。为了促进用户交互，Co-STORM 通过将发现的信息组织成动态思维导图来帮助用户跟踪讨论过程，最终生成一份综合报告作为学习成果。为了进行自动评估，我们通过收集带有用户目标的真实信息寻求记录构建了 WildSeek 数据集。Co-STORM 在讨论轨迹和报告质量方面均优于基线方法。在进一步的人类评估中，70% 的参与者相比搜索引擎更偏好 Co-STORM，78% 的参与者相比 RAG 聊天机器人更青睐 Co-STORM。
英文摘要：
While language model (LM)-powered chatbots and generative search engines excel at answering concrete queries, discovering information in the terrain of unknown unknowns remains challenging for users. To emulate the common educational scenario where children/students learn by listening to and participating in conversations of their parents/teachers, we create Collaborative STORM (Co-STORM). Unlike QA systems that require users to ask all the questions, Co-STORM lets users observe and occasionally steer the discourse among several LM agents. The agents ask questions on the user's behalf, allowing the user to discover unknown unknowns serendipitously. To facilitate user interaction, Co-STORM assists users in tracking the discourse by organizing the uncovered information into a dynamic mind map, ultimately generating a comprehensive report as takeaways. For automatic evaluation, we construct the WildSeek dataset by collecting real information-seeking records with user goals. Co-STORM outperforms baseline methods on both discourse trace and report quality. In a further human evaluation, 70% of participants prefer Co-STORM over a search engine, and 78% favor it over a RAG chatbot.
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。
