Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
Authors: name: Zihao Yi; name: Qingxuan Jiang; name: Ruotian Ma; name: Xingyu Chen; name: Qu Yang; name: Mengru Wang; name: Fanghua Ye; name: Ying Shen; name: Zhaopeng Tu; name: Xiaolong Li; name: Linus
Published: 2025-11-07 03:50:52

Abstract: Large Language Models (LLMs) are increasingly tasked with creative
generation, including the simulation of fictional characters. However, their
ability to portray non-prosocial, antagonistic personas remains largely
unexamined. We hypothesize that the safety alignment of modern LLMs creates a
fundamental conflict with the task of authentically role-playing morally
ambiguous or villainous characters. To investigate this, we introduce the Moral
RolePlay benchmark, a new dataset featuring a four-level moral alignment scale
and a balanced test set for rigorous evaluation. We task state-of-the-art LLMs
with role-playing characters from moral paragons to pure villains. Our
large-scale evaluation reveals a consistent, monotonic decline in role-playing
fidelity as character morality decreases. We find that models struggle most
with traits directly antithetical to safety principles, such as ``Deceitful''
and ``Manipulative'', often substituting nuanced malevolence with superficial
aggression. Furthermore, we demonstrate that general chatbot proficiency is a
poor predictor of villain role-playing ability, with highly safety-aligned
models performing particularly poorly. Our work provides the first systematic
evidence of this critical limitation, highlighting a key tension between model
safety and creative fidelity. Our benchmark and findings pave the way for
developing more nuanced, context-aware alignment methods.