Researchers Release ThoughtTrace Dataset Capturing User Thoughts During LLM Interactions

What are users thinking during their interactions with LLMs? We introduce ThoughtTrace — the first large-scale dataset that captures what users think during real-world human–AI conversations, not just what they type. → 10,174 thought annotations → 2,155 multi-turn conversations, 17,058 turns → 1,058 users → 20 LLMs These thoughts improve user behavior prediction (+41.7%) and model alignment (+25.6%). This opens a new paradigm of user-centric LLM research. Full information in the thread 🧶 Read our paper: https://arxiv.org/abs/2605.20087 Check our project website: https://thoughttrace-project.github.io/