analiza aiqbench
This study reveals that biographical personas in system prompts act as model-dependent behavioral biases rather than universal quality enhancers for code generation, significantly degrading performance on Claude Opus while leaving GPT-5.5 unaffected. The findings underscore the critical need to evaluate prompt engineering strategies under controlled conditions across different frontier models before deploying them at scale.
The Librarian Who Refused to Code: Model-Dependent Identity Enactment in LLM Code Generation
arXiv:2607.17420v1 Announce Type: new Abstract: Biographical personas are widely used in system prompts, but their effects on code generation are rarely evaluated under controlled, pre-registered conditions. We tested four prompt conditions (no persona, two engineer personas, and a research-librarian persona), 12 code-generation tasks, two frontier models, and five runs per cell (480 completions). Persona effects differed between the two tested models. Under the pre-registered mixed-effects analysis, the condition-by-model interaction was significant for provider-reported output tokens; a post-hoc visible-character measure showed the same qualitative pattern. Six GPT-5.5 completions were length-capped and are reported separately. On Claude Opus, the minimalist engineer persona reduced visible output by 30% (33% in provider tokens) without improving correctness, while the thorough engineer persona increased output without a correctness gain. In an exploratory post-hoc analysis, the librarian persona elicited in-character disclaimers in 55 of 60 Opus responses and 12 genuine no-code responses, lowering mean correctness from 0.92 to 0.67. GPT-5.5 produced neither behavior in its 59 non-truncated responses. These results are consistent with personas acting as Model-Dependent behavioral-policy biases rather than universal quality interventions. We release raw completions, derived scores, analysis artifacts, a pre-registration document, and an execution gate log; end-to-end test-based rescoring requires an unreleased task harness.
Abstract:Biographical personas are widely used in system prompts, but their effects on code generation are rarely evaluated under controlled, pre-registered conditions. We tested four prompt conditions (no persona, two engineer personas, and a research-librarian persona), 12 code-generation tasks, two frontier models, and five runs per cell (480 completions). Persona effects differed between the two tested models. Under the pre-registered mixed-effects analysis, the condition-by-model interaction was significant for provider-reported output tokens; a post-hoc visible-character measure showed the same qualitative pattern. Six GPT-5.5 completions were length-capped and are reported separately. On Claude Opus, the minimalist engineer persona reduced visible output by 30% (33% in provider tokens) without improving correctness, while the thorough engineer persona increased output without a correctness gain. In an exploratory post-hoc analysis, the librarian persona elicited in-character disclaimers in 55 of 60 Opus responses and 12 genuine no-code responses, lowering mean correctness from 0.92 to 0.67. GPT-5.5 produced neither behavior in its 59 non-truncated responses. These results are consistent with personas acting as Model-Dependent behavioral-policy biases rather than universal quality interventions. We release raw completions, derived scores, analysis artifacts, a pre-registration document, and an execution gate log; end-to-end test-based rescoring requires an unreleased task harness.
17 pages, 3 tables, no figures. Ancillary files include raw completions, derived scores, analysis scripts, persona texts, and preregistration
Computation and Language (cs.CL); Software Engineering (cs.SE)
arXiv:2607.17420 [cs.CL]
(or arXiv:2607.17420v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.17420 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
LOCKED_MODELS.txt
analysis/analyze_main.py
analysis/c2_by_cell.csv
analysis/identity_enactment.py
analysis/mixed_effects.json
analysis/mixed_effects_corrected.json
analysis/recompute_corrected.py
analysis/requirements.txt
analysis/results.csv
analysis/score_responses.py
analysis/scored_responses.csv
personas/P0_baseline.txt
personas/P_A_maya.txt
personas/P_B_ron.txt
personas/P_C_linnea.txt
preregistration/05_preregistration.md
runs/main/main_claude-opus-4-8_20260622T145939232171Z.jsonl
runs/main/main_gpt-5_5_20260622T145642342609Z.jsonl
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