LLM personas impact code generation differently across models

A new research paper explores how biographical personas in system prompts affect LLM code generation, finding that these effects are model-dependent. The study tested four persona conditions across two frontier models, GPT-5.5 and Claude Opus, on various coding tasks. Results indicated that personas can act as model-specific behavioral biases rather than universal quality enhancers, with the librarian persona notably eliciting in-character disclaimers and refusals to code on Claude Opus. AI

IMPACT Understanding how system prompts influence LLM behavior is crucial for optimizing AI assistants and ensuring reliable code generation.

RANK_REASON Research paper analyzing LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Claude Opus
• GPT-5.5
• The Librarian Who Refused to Code: Model-Dependent Identity Enactment in LLM Code Generation

• paper
• model release

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