7 papers · 1 filter
CoSER: A Comprehensive Literary Dataset and Framework for Training and Evaluating LLM Role-Playing and Persona Simulation
Xintao Wang, Heng Wang, Yifei Zhang +9
Role-playing language agents (RPLAs) have emerged as promising applications of large language models (LLMs). However, simulating established characters presents a challenging task…
Curse of Knowledge: When Complex Evaluation Context Benefits yet Biases LLM Judges
Weiyuan Li, Xintao Wang, Siyu Yuan +5
As large language models (LLMs) grow more capable, they face increasingly diverse and complex tasks, making reliable evaluation challenging. The paradigm of LLMs as judges has emer…
Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data
Yiting Ran, Xintao Wang, Rui Xu +4
Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia.While existing RPAs…
From Persona to Personalization: A Survey on Role-Playing Language Agents
Jiangjie Chen, Xintao Wang, Rui Xu +15
Recent advancements in large language models (LLMs) have significantly boosted the rise of Role-Playing Language Agents (RPLAs), i.e., specialized AI systems designed to simulate a…
Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works
Xinfeng Yuan, Siyu Yuan, Yuhan Cui +5
Large language models (LLMs) have demonstrated impressive performance and spurred numerous AI applications, in which role-playing agents (RPAs) are particularly popular, especially…
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
Xintao Wang, Yunze Xiao, Jen-tse Huang +10
Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accuratel…