人格引导的LLM智能体在任务导向对话中的研究

AI 摘要

肯塔基大学的研究人员提出了一种无需训练的任务导向对话框架，通过两个LLM智能体模拟用户与系统交互，研究人格适应对对话质量的影响。实验在SGD数据集上评估了GPT-4o、Qwen3-Next-80B和Gemini 2.0 Flash，发现人格适应能提升约束满足率和用户满意度，但会降低真实性，而基于线索的隐式适应（Try）能最好地平衡这一权衡。

正文节选

Persona-Guided LLM Agents for Task-Oriented Dialogue Abstract Prior work has shown that large language models (LLMs) can express diverse personality traits in open-ended text generation. However, it remains unclear whether they can do so in a goal-directed dialogue without compromising task completion, and whether adapting to the user’s personality improves the interaction quality. We study these questions in task-oriented dialogue (TOD), where a system helps a user accomplish a goal via multi-t

发布时间：2026-08-20 12:00

抓取时间：2026-08-20 12:02

来源机构：arXiv