基于记忆的角色扮演：评估和增强LLM中人设知识的利用
Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs
AI总结：
本文提出Memory-Driven Role-Playing框架，通过MREval、MRPrompt和MRBench评估LLM在角色扮演中的记忆能力，验证了小模型能效媲美大模型，并证明上游记忆提升下游响应质量。
摘要：
忠实LLM角色扮演的核心挑战是维持长对话中的一致性，因模型常无法准确回忆和应用其指定的人设知识。为此，我们提出Memory-Driven Role-Playing范式，受斯坦尼斯拉夫斯基
英文摘要：
A core challenge for faithful LLM role-playing is sustaining consistent characterization throughout long, open-ended dialogues, as models frequently fail to recall and accurately apply their designated persona knowledge without explicit cues. To tackle this, we propose the Memory-Driven Role-Playing paradigm. Inspired by Stanislavski's "emotional memory" acting theory, this paradigm frames persona knowledge as the LLM's internal memory store, requiring retrieval and application based solely on dialogue context, thereby providing a rigorous test of depth and autonomous use of knowledge. Centered on this paradigm, we contribute: (1) MREval, a fine-grained evaluation framework assessing four memory-driven abilities - Anchoring, Recalling, Bounding, and Enacting; (2) MRPrompt, a prompting architecture that guides structured memory retrieval and response generation; and (3) MRBench, a bilingual (Chinese/English) benchmark for fine-grained diagnosis. The novel paradigm provides a comprehensive diagnostic for four-staged role-playing abilities across 12 LLMs. Crucially, experiments show that MRPrompt allows small models (e.g., Qwen3-8B) to match the performance of much larger closed-source LLMs (e.g., Qwen3-Max and GLM-4.7), and confirms that upstream memory gains directly enhance downstream response quality, validating the staged theoretical foundation.
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。
