Prompting Against Persona Drift: Comparing Intervention Timing and Content in LLM-Simulated Conversations

2026-09-21

AI Summary

研究通过五种提示机制解决LLM模拟对话中学生角色一致性下降问题，发现行为特异性指令最有效，但未完全消除漂移。

Abstract

Simulating student personas with large language models (LLMs) enables scalable evaluation of educational systems. However, behavioral drift, a progressive decline in persona consistency, can emerge over extended conversations, limiting the validity of such simulations. We evaluate five prompt-level mechanisms using separate monitoring and intervention pipelines. Across 1,200 28-turn conversations spanning four LLMs and two ADHD persona intensities, we varied when to intervene (static vs. adaptive) and what to inject (reinjection vs. reflective reminder), plus a novel adaptive condition in which a monitor generates behavior-specific instructions. Relative to no intervention, reinjection reduced the modeled rate of LLM-rated drift by 35--38\%, reflective reminders by 22--27\%, and behavior-specific instruction by 87\%. None eliminated drift. We found no evidence that adaptive timing outperformed static scheduling. Monitoring therefore appears more useful for deciding \textit{what} to correct than \textit{when} to intervene, although behavior-specific instruction requires component-level testing.

Problem

• behavioral drift
• persona consistency
• large language models
• simulated conversations

Innovation

• behavior-specific instruction
• adaptive condition
• persona drift reduction