Personas in LLM Prompts Affect Code Generation Differently Across Models
A controlled study evaluated the impact of biographical personas in system prompts on code generation by large language models (LLMs). The research found that persona effects varied by model: on Claude Opus, a librarian persona led to frequent in-character disclaimers and a drop in code correctness from 0.92 to 0.67, while GPT-5.5 did not exhibit these behaviors. The findings indicate that personas introduce model-dependent behavioral biases rather than consistent quality improvements.
Why it matters: This work highlights that persona-based prompting can unpredictably affect LLM performance, emphasizing the importance of model-specific prompt engineering.
Full story at: arXiv Computation and Language ↗