Dynamic Context Adaptation for Consistent Role-Playing Agents with Retrieval-Augmented Generations

Jeiyoon Park; Yongshin Han; Minseop Kim; Kisu Yang

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arXiv · 2508.02016

Dynamic Context Adaptation for Consistent Role-Playing Agents with Retrieval-Augmented Generations

Jeiyoon Park·Yongshin Han·Minseop Kim·Kisu Yang

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Abstract

Building role-playing agents (RPAs) that faithfully emulate specific characters remains challenging because collecting character-specific utterances and continually updating model parameters are resource-intensive, making retrieval-augmented generation (RAG) a practical necessity. However, despite the importance of RAG, there has been little research on RAG-based RPAs. For example, we empirically find that when a persona lacks knowledge relevant to a given query, RAG-based RPAs are prone to hallucination, making it challenging to generate accurate responses. In this paper, we propose Amadeus, a training-free framework that can significantly enhance persona consistency even when responding to questions that lie beyond a character's knowledge. In addition, to underpin the development and rigorous evaluation of RAG-based RPAs, we manually construct CharacterRAG, a role-playing dataset that consists of persona documents for 15 distinct fictional characters totaling 976K written characters, and 450 question-answer pairs. We find that our proposed method effectively models not only the knowledge possessed by characters, but also various attributes such as personality.

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Jeiyoon Park, Yongshin Han, Minseop Kim, Kisu Yang. 2025-08-04. Dynamic Context Adaptation for Consistent Role-Playing Agents with Retrieval-Augmented Generations. https://arxiv.org/abs/2508.02016

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