做对话AI研究和产品开发的团队，终于有了一个能捕捉用户真实想法的数据集——ThoughtTrace帮你理解用户为什么发那条消息、对回复的真实感受，值得用来改进助手对齐和个性化。
现有AI对话数据集仅记录用户说了什么，但忽略了用户在想什么。ThoughtTrace是首个大规模数据集，包含1,058名用户、2,155次对话、17,058轮交互和10,174条思维标注，覆盖20种语言模型。研究发现，用户的思维与消息内容在语义上截然不同，前沿LLM难以从上下文中推断，且思维内容多样、与对话阶段相关。该数据集可用于改进用户行为预测和训练个性化助手，为构建更理解用户潜在目标的AI系统奠定基础。
ThoughtTrace: Understanding User Thoughts in Real-World LLM Interactions
Conversational AI has now reached billions of users, yet existing datasets capture only what people say, not what they think. We introduce ThoughtTrace, the first large-scale dataset that pairs real-world multi-turn human--AI conversations with users' self-reported thoughts: their reasons for sending prompts and reactions to assistant responses. ThoughtTrace comprises 1,058 users, 2,155 conversations, 17,058 turns, and 10,174 thought annotations collected across 20 language models. Our analysis shows that ThoughtTrace captures long-horizon, topically diverse interactions, and that thoughts are semantically distinct from messages, difficult for frontier LLMs to infer from context, diverse in content, and tied to conversation stages. We further demonstrate the utility of thoughts for downstream modeling. First, thoughts improve user-behavior prediction as inference-time context. Second, thought-guided rewrites provide fine-grained alignment signals for training personalized assistants. Together, ThoughtTrace establishes user thoughts as a new data modality for studying the cognitive dynamics behind human--AI interaction and provides a foundation for building assistants that better understand and adapt to users' latent goals, preferences, and needs.