Recent advances in LLM-based role-playing language agents (RPLAs) have attracted broad attention in various applications. While chain-of-thought reasoning has shown importance in many tasks for LLMs, the internal thinking processes of RPLAs remain unexplored. Understanding characters' inner thoughts is crucial for developing advanced RPLAs. In this paper, we introduce ROLETHINK, a novel benchmark constructed from literature for evaluating character thought generation. We propose the task of inner thought reasoning, which includes two sets: the gold set that compares generated thoughts with original character monologues, and the silver set that uses expert synthesized character analyses as references. To address this challenge, we propose MIRROR, a chain-of-thought approach that generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations. Through extensive experiments, we demonstrate the importance of inner thought reasoning for RPLAs, and MIRROR consistently outperforms existing methods. Resources are available at https://github.com/airaer1998/RPA_Thought.

本研究填补了角色扮演语言代理（RPLA）内在思维过程未被探索的空白，提出了一个名为ROLETHINK的新基准，用于评估角色思维生成。通过提出内在思维推理任务及应用MIRROR方法，我们的实验表明，内在思维推理对RPLA至关重要，并且MIRROR方法在性能上持续优于现有技术。

猜猜我在想什么：角色扮演语言代理的内在思维推理基准