Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
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.
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Zihao Yi
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Qingxuan Jiang
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Ruotian Ma
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Xingyu Chen
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Qu Yang
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2026-01-27T09:44:30.957Z
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Qu Yang
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user
quyang22
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Mengru Wang
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Fanghua Ye
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Ying Shen
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Zhaopeng Tu
claimed_verified
2025-11-10T21:03:03.510Z
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Zhaopeng Tu
zptu
user
zptu
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Xiaolong Li
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Linus
Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
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.
LLMs struggle to authentically portray morally ambiguous or villainous characters due to safety alignment, as evidenced by the Moral RolePlay benchmark.
Large Language Models Moral RolePlay benchmark moral alignment scale role-playing fidelity safety principles Deceitful Manipulative chatbot proficiency model safety creative fidelity