The preprint asks whether instructions describing the Big Five traits change eight decision effects in GPT-4o, GPT-4o-mini, Llama 3-70B, and Llama 3-8B, and whether a verbal warning reduces those effects. Personality needs precise interpretation here: the study neither measures an inherent model trait nor validates that a model adopts a psychometric profile. It prepends a description of Openness, Conscientiousness, Extraversion, Agreeableness, or Neuroticism, or a supposedly reversed description. The material combines 13,465 university-admission prompts reused from Echterhoff et al. for anchoring, framing, status quo, and group attribution with 4,585 GPT-4-generated BiasEval scenarios for decoy, risk aversion, sunk cost, and endowment effects. All four models run at temperature zero under baseline, trait, and reversed-trait conditions; mitigation literally adds the sentence, “Be mindful of not being biased by cognitive bias.” Each effect is a difference in rates except endowment, which normalizes the willingness-to-accept minus willingness-to-pay difference by a control valuation. Mitigation is the reduction in absolute effect relative to baseline, so a negative mitigation value means that magnitude increased. Results depend strongly on architecture and prompt. For anchoring, GPT-4o moves from 0.112 without a trait to 0.338 under Agreeableness, while Llama 3-70B starts at -0.243 and remains negative under all five traits. For framing, GPT-4o reduces a -0.063 baseline difference to between -0.008 and 0.002, but Llama 3-70B increases the magnitude to -0.181 under Neuroticism. Decoy baselines range from -0.385 for GPT-4o-mini to 0.128 for Llama 3-70B; a negative value is a shift opposite the predicted decoy direction, not absence of sensitivity. Risk-aversion baselines are 0.044, 0.544, 0.428, and 0.074 for GPT-4o, GPT-4o-mini, Llama 3-70B, and Llama 3-8B, with trait prompts sometimes increasing and sometimes decreasing them. Sunk cost is almost always zero, except 0.008 and 0.019 for Extraversion and Agreeableness in GPT-4o. Status-quo baselines are 0.134, -0.101, -0.320, and -0.004, again with heterogeneous directions. Endowment changes are extreme: published baselines are 4.27%, 23.07%, 91.23%, and 39.77%, while some trait conditions reach 109.59% for GPT-4o and 124.37% for Llama 3-8B. Baseline gender group-attribution effects are small (-0.002, -0.018, 0.019, and 0). The paper concludes that Conscientiousness and Agreeableness tend to help the awareness warning. Recomputing its own descriptive figure gives mean reductions of 0.0305 for Conscientiousness and 0.0246 for Agreeableness over 24 rows each, but the figure duplicates every framing value under status quo for all four models. No tests, intervals, standard errors, or independent replications justify using “significantly” in the statistical sense. Reproducibility is also inadequate: although the appendix promises detailed prompts, it leaves `{context}`, `{question}`, and other placeholders instead of the actual personality, reversed-personality, and scenario text; BiasEval, generations, code, model snapshots, and API dates are not released. The summary table omits Openness for GPT-4o-mini anchoring; the detailed table provides neither a baseline nor mitigation values for Llama 3-8B anchoring even though the main table reports them; and the detailed sunk-cost table omits GPT-4o-mini. The defensible contribution is evidence that outputs on synthetic decision tasks change substantially with instructions and architecture. It does not establish stable model traits, psychological causation, general efficacy of any personality for debiasing, or safety in real decisions.
Research question
How do eight measures of bias or decision effect change when four LLMs are instructed to represent normal or inverted Big Five traits, and to what extent does a zero-shot warning not to be biased reduce the magnitude of those effects?