Before You Say It: Anticipating Verbal Behavior from Longitudinal Everyday Conversations with LLMs

2026-08-13

AI Summary

This study addresses the longstanding challenge of making long-term, personalized prospective predictions of individuals’ future speech behaviors from everyday conversations—a capability essential for timely detection of goal-deviant or potentially inappropriate utterances. For the first time, the authors demonstrate that large language models (LLMs) trained on over 1,000 hours of real-world longitudinal dialogue data collected via wearable devices can effectively forecast specific verbal behaviors in subsequent interactions. Through semi-structured user interviews, the research not only validates the efficacy of LLMs in personalized behavioral prediction but also uncovers their practical potential for proactive behavioral support and intervention. These findings establish a novel paradigm for developing personalized AI systems endowed with predictive foresight.

Abstract

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.

Problem

• behavioral prediction
• longitudinal conversation
• verbal behavior
• anticipatory AI
• personalized modeling

Innovation

• predictive behavioral modeling
• longitudinal conversation
• large language models (LLMs)
• anticipatory AI
• wearable data collection