探测大型语言模型中稳定内部信念的缺乏
Probing the Lack of Stable Internal Beliefs in LLMs
AI总结：
本研究探讨了LLM在多轮交互中维持隐含一致性的能力，发现其隐含目标易随对话变化，需引入机制以锚定长期目标，以提升对话系统中的人格建模效果。
摘要：
基于角色驱动的大型语言模型（LLMs）需要在交互中保持一致的行为倾向以模拟人类特质，如坚持或可靠性。然而，当前LLM在长时间对话中缺乏稳定的内部表示来支撑其响应。本文探讨LLM能否在多轮交互中维持'隐含一致性'，即持续遵循未明示目标。我们设计了一个20题式谜语游戏范式，要求LLM秘密选择目标并用'是/否'回答用户猜测。评估发现，LLM难以保持隐含一致性：其隐含'目标'在回合间变化，除非上下文明确提供所选目标。这些发现突显了角色驱动LLM构建的关键限制，并强调了需要机制以在时间上锚定隐含目标，这是实现对话系统等交互应用中真实人格建模的关键。
英文摘要：
Persona-driven large language models (LLMs) require consistent behavioral tendencies across interactions to simulate human-like personality traits, such as persistence or reliability. However, current LLMs often lack stable internal representations that anchor their responses over extended dialogues. This work explores whether LLMs can maintain "implicit consistency", defined as persistent adherence to an unstated goal in multi-turn interactions. We designed a 20-question-style riddle game paradigm where an LLM is tasked with secretly selecting a target and responding to users' guesses with "yes/no" answers. Through evaluations, we find that LLMs struggle to preserve latent consistency: their implicit "goals" shift across turns unless explicitly provided their selected target in context. These findings highlight critical limitations in the building of persona-driven LLMs and underscore the need for mechanisms that anchor implicit goals over time, which is a key to realistic personality modeling in interactive applications such as dialogue systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
