RoleRAG: Enhancing LLM Role-Playing via Graph Guided Retrieval

Yongjie Wang; Jonathan Leung; Zhiqi Shen

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arXiv · 2505.18541

RoleRAG: Enhancing LLM Role-Playing via Graph Guided Retrieval

Yongjie Wang·Jonathan Leung·Zhiqi Shen

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Abstract

Large Language Models (LLMs) have shown promise in character imitation, enabling immersive and engaging conversations. However, they often generate content that is irrelevant or inconsistent with a character's background. We attribute these failures to: (1) the inability to accurately recall character-specific knowledge due to entity ambiguity, and (2) a lack of awareness of the character's cognitive boundaries. To address these issues, we propose RoleRAG, a retrieval-based framework that integrates efficient entity disambiguation for knowledge indexing with a boundary-aware retriever for extracting contextually appropriate information from a structured knowledge graph. Experiments on role-playing benchmarks show that RoleRAG's calibrated retrieval helps both general-purpose and role-specific LLMs better align with character knowledge and reduce hallucinated responses.

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Yongjie Wang, Jonathan Leung, Zhiqi Shen. 2025-05-24. RoleRAG: Enhancing LLM Role-Playing via Graph Guided Retrieval. https://arxiv.org/abs/2505.18541

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