As Large-scale Language Models (LLMs) advance, the development of engaging Role-Playing Conversational Agents (RPCAs) has gained prominence. Despite this progress, there is a notable absence of benchmarks designed around dialogues, rather than question-answering formats, to assess the effectiveness of RPCA interactions. This paper introduces the RAIDEN benchmark, containing a comprehensive dataset specifically developed for RPCA evaluation, comprising over 40,000 multi-turn utterances across 135 characters. The benchmark focuses on assessing particular dimensions at different stages of a conversation, facilitated through interactions conducted by annotators. This approach allows the evaluation phase to concentrate on specific response dimensions, and thus subjectivity in dialogue evaluation is reduced. To further enhance objectivity, evaluators compare responses from two different models rather than assessing a single response in isolation. Besides, we introduce RPCAJudger, a specialized judging LLM tailored for automatic RPCA evaluation. The evaluations conducted by RPCAJudger closely mirror human judgments, and its API-free methodology serves to prevent potential data leakage. All the models and all non-private leaderboard data will be made publicly available.

Large Language Models (LLMs) have demonstrated significant advancements in various fields, notably in Role-Playing Conversational Agents (RPCAs). However, when confronted with role-specific professional inquiries, LLMs-based RPCAs tend to underperform due to their excessive emphasis on the conversational abilities of characters rather than effectively invoking and integrating relevant expert knowledge. This often results in inaccurate responses. We refer to this phenomenon as the “Knowledge Misalignment” which underscores the limitations of RPCAs in integrating expert knowledge. To mitigate this issue, we have introduced an Anchoring-Guidance Fine-Tuning (AnGFT) Framework into the RPCAs’ training process. This involves initially linking the Anchoring-Based System Prompt (ASP) with the LLM’s relevant expert domains through diverse prompt construction strategies and supervised fine-tuning (SFT). Following the role-play enriched SFT, the integration of ASP enables LLMs to better associate with relevant expert knowledge, thus enhancing their response capabilities in role-specific expert domains. Moreover, we have developed four comprehensive metrics—helpfulness, thoroughness, credibility, and feasibility—to evaluate the proficiency of RPCAs in responding to professional questions. Our method was tested across four professional fields, and the experimental outcomes suggest that the proposed AnGFT Framework substantially improves the RPCAs’ performance in handling role-specific professional queries, while preserving their robust role-playing abilities.