在你开口前：基于与大语言模型（LLM）的日常纵向对话预测言语行为
Before You Say It: Anticipating Verbal Behavior from Longitudinal Everyday Conversations with LLMs
AI总结：
本研究提出基于LLM的预测性行为建模方法，通过14名参与者超1000小时的纵向对话数据集，证实可预测特定用户言语行为，为构建个性化AI系统提供新方向。
AI中文摘要：
深度了解一个人不仅要理解其言行，还要预判其在不同情境下的思考、反应与参与方式，这类预测可帮助系统提前察觉用户偏离目标的情况、在用户做出遗憾行为前加以干预、在盲点形成前将其呈现。尽管许多交互系统会对用户建模以实现更个性化的交互，但多数无法做出此类行为预测，因为这通常需要对用户在各类日常情境下的行为展开纵向观察与推断。本研究提出一种基于大语言模型（LLM）的新型预测性行为建模方法，用于预判用户在日常对话情境下的可能行为：（1）通过可穿戴智能手表收集14名参与者超过1000小时的自然对话纵向数据集；（2）将基于LLM的预测与真实行为进行对比评估；（3）采用半结构化访谈探究参与者对行为预测的看法及对未来行为支持形式的观点。综上，研究结果证实，可通过纵向对话数据预测特定用户的言语行为，这为未来构建情境感知、预测性、主动性且个性化的AI系统开辟了新可能。
英文摘要：
Knowing someone deeply means not just understanding what they say or do but also how they will likely think, react, and engage across situations. Such predictions could eventually inform systems to anticipate when the individual is about to deviate from their goal, catch regrettable behaviors before they are made, and surface blind spots before they take hold. While many interactive systems model users to enable more personalized interactions, most cannot make such behavioral predictions, as this often requires longitudinal observation and inference of how the individual's behaviors unfold across various everyday situations. In this work, we introduce a novel LLM-based predictive behavioral modeling approach that anticipates a user's likely behavior across everyday conversational situations. We (1) collect a longitudinal dataset of over 1000 hours of naturalistic conversations from 14 participants using a wearable smartwatch; (2) evaluate LLM-based predictions against ground truth behaviors; and (3) use semi-structured interviews to explore participants perceptions of behavioral predictions and their views on possible forms of future behavioral support. Altogether, our findings provide evidence that person-specific verbal behavior can be predicted from longitudinal conversational data. This opens up new possibilities for potential future context-aware, anticipatory, proactive and personalized AI systems.
