Does safety alignment harm models' ability to roleplay villains?

The Moral RolePlay benchmark (800 characters across 4 moral levels) reveals a consistent, monotonic decline in role-playing fidelity as character morality decreases. Average scores drop from 3.21 for moral paragons to 2.62 for villains. The most significant degradation occurs at the boundary between "flawed-but-good" and "egoistic" characters — suggesting that simulating self-serving behavior, not evil per se, is the primary obstacle.

Models are most penalized for failing to portray traits directly antithetical to safety principles: Manipulative, Deceitful, and Cruel. Instead of nuanced malevolence, they substitute superficial aggression — producing villains who are loud and angry rather than strategically deceptive. General chatbot proficiency (Arena leaderboard ranking) is a poor predictor of villain role-playing ability, with highly safety-aligned models performing particularly poorly.

This has direct implications for the False Punditry argument. Since What anchors a stable identity beneath an LLM's persona?, LLMs cannot take genuine stances — including adversarial ones. The inability to convincingly portray a villain is the flip side of the inability to take a genuine controversial position in punditry: both require committing to a perspective that may be socially costly, which alignment training systematically suppresses.

Since Can language models distinguish expert arguments from common assumptions?, the villain-fidelity finding adds an empirical dimension: models cannot even simulate the kind of committed, stake-holding stance that genuine expertise (and genuine villainy) requires.

Source: Role Play Paper: Too Good to be Bad: On the Failure of LLMs to Role-Play Villains