发表机构
Tsinghua University; Nankai University; University of Cambridge（清华大学; 南开大学; 剑桥大学）
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TraceMind，利用低成本交互轨迹预测用户在人机内容协同生成中对原子信息单元的识别级吸收，实验表明其优于基线，并揭示持续主动参与是关键证据。
AI 中文摘要
在人类与大语言模型（LLM）的内容协同生成中，AI生成的信息可能未经用户充分处理便进入最终产物，从而在产物被分享或据此行动时带来风险。我们研究在开放式协同生成中，能否评估原子信息单元的识别级吸收，并利用低成本交互轨迹进行预测。我们收集了62名参与者在三项任务中的数据。对于每份最终草稿，我们提取原子信息单元并生成任务后识别问题，共获得1187个单元级吸收标签。我们提出了TraceMind，它在聊天和草稿历史中追踪单元，将交互轨迹与变化的屏幕布局对齐，并建模空间、时间及工作流信息证据。TraceMind在AUROC、AUPRC-non、平衡准确率和宏F1分数上均优于所有学习基线。我们发现，吸收贯穿整个交互过程，持续的主动参与提供了超越孤立信号的信息性证据。我们的工作将人机协同生成从内容采纳转向用户实际吸收的内容，推动了基于低成本交互轨迹的吸收感知系统的开发。
英文摘要
In human-LLM content co-generation, AI-generated information can enter final artifacts without being adequately processed by users, creating risks when artifacts are shared or acted upon. We study whether recognition-level uptake of atomic information units can be assessed in open-ended co-generation and predicted from low-cost interaction traces. We collected data from 62 participants across three tasks. For each final draft, we extracted atomic information units and generated post-task recognition questions, yielding 1187 unit-level uptake labels. We present TraceMind, which tracks units across Chat and Draft histories, aligns interaction traces with changing on-screen layouts, and models spatial, temporal, and workflow-informed evidence. TraceMind outperformed all learned baselines across AUROC, AUPRC-non, balanced accuracy, and macro-F1. We found that uptake unfolds throughout interaction, with sustained active engagement providing informative evidence beyond isolated signals. Our work shifts human-LLM co-generation from content adoption toward what users actually take up, motivating uptake-aware systems grounded in low-cost interaction traces.
