Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works

• Main Authors: Yuan, Xinfeng, Yuan, Siyu, Cui, Yuhan, Lin, Tianhe, Wang, Xintao, Xu, Rui, Chen, Jiangjie, Yang, Deqing
• Format: Journal Article
• Language: English
• Published: 19-04-2024
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2404.12726

Large language models (LLMs) have demonstrated impressive performance and
spurred numerous AI applications, in which role-playing agents (RPAs) are
particularly popular, especially for fictional characters. The prerequisite for
these RPAs lies in the capability of LLMs to understand characters from
fictional works. Previous efforts have evaluated this capability via basic
classification tasks or characteristic imitation, failing to capture the
nuanced character understanding with LLMs. In this paper, we propose evaluating
LLMs' character understanding capability via the character profiling task,
i.e., summarizing character profiles from corresponding materials, a widely
adopted yet understudied practice for RPA development. Specifically, we
construct the CroSS dataset from literature experts and assess the generated
profiles by comparing them with ground truth references and evaluating their
applicability in downstream tasks. Our experiments, which cover various
summarization methods and LLMs, have yielded promising results. These results
strongly validate the character understanding capability of LLMs. Resources are
available at https://github.com/Joanna0123/character_profiling.