Papers › CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds

CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds

7 Dec 2024arXiv:2412.05631archive 2025-07-28

Lei Wang, Jianxun Lian, Yi Huang, Yanqi Dai, Haoxuan Li, Xu Chen, Xing Xie, Ji-Rong Wen

Role-playing is a crucial capability of Large Language Models (LLMs), enabling a wide range of practical applications, including intelligent non-player characters, digital twins, and emotional companions. Evaluating this capability in LLMs is challenging due to the complex dynamics involved in role-playing, such as maintaining character fidelity throughout a storyline and navigating open-ended narratives without a definitive ground truth. Current evaluation methods, which primarily focus on question-answering or conversational snapshots, fall short of adequately capturing the nuanced character traits and behaviors essential for authentic role-playing. In this paper, we propose CharacterBox, which is a simulation sandbox designed to generate situational fine-grained character behavior trajectories. These behavior trajectories enable a more comprehensive and in-depth evaluation of role-playing capabilities. CharacterBox consists of two main components: the character agent and the narrator agent. The character agent, grounded in psychological and behavioral science, exhibits human-like behaviors, while the narrator agent coordinates interactions between character agents and environmental changes. Additionally, we introduce two trajectory-based methods that leverage CharacterBox to enhance LLM performance. To reduce costs and facilitate the adoption of CharacterBox by public communities, we fine-tune two smaller models, CharacterNR and CharacterRM, as substitutes for GPT API calls, and demonstrate their competitive performance compared to advanced GPT APIs.

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extract_scores paitesanshi/characterbox/evaluate.py official repository unverified MIT (permissive) · fd6ba1a21f12998e · report
extract_scores paitesanshi/characterbox/evaluate_narrator.py official repository unverified MIT (permissive) · a4ceb5e434c0e21e · report
extract_scores paitesanshi/characterbox/evaluate_scene.py official repository unverified MIT (permissive) · 3798794817974d8f · report
get_num_tokens paitesanshi/characterbox/convert_character_data.py official repository unverified MIT (permissive) · 9131ad128fc2d2ed · report
get_num_tokens paitesanshi/characterbox/evaluate_narrator.py official repository unverified MIT (permissive) · 1937f9277b291c92 · report

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AdamAttentionAttention DropoutBPECosine AnnealingDense ConnectionsDiscriminative Fine-TuningDropoutFocusGPTLayer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionResidual ConnectionSoftmaxWeight Decay

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