This preprint asks whether explicit personality descriptions change gender-stereotypical language in English and Hindi occupational artifacts. It crosses six LLMs, 50 India-grounded occupations, two languages, persona gender, and 18 trait conditions, high and low descriptions for six HEXACO and three Dark Triad dimensions, plus no-personality baselines. Its metric embeds each sentence with IndicSBERT and subtracts similarity to female from male stereotype centroids; each story receives the signed score of its maximum-absolute-bias sentence. The published results associate Machiavellianism and Psychopathy with positive male-stereotypical shifts, Narcissism with a smaller shift, and HEXACO with a heterogeneous pattern: Honesty-Humility, Agreeableness, and Conscientiousness also increase male alignment, while Openness and Emotionality reduce it and Extraversion is not significant. Hindi has a more male-leaning baseline in five of six models, whereas personality modulation is larger in English. Three annotators judge the conditioned story more stereotyped in 66% of 50 English pairs and 72% of 50 Hindi pairs. The audit confirms exactly 23,400 public rows but not 23,400 valid distinct stories: 17 stories are empty and 126 excess duplicates in 45 groups reuse text across conditions; one story appears 19 times, and some female-labeled rows retain male pronouns. The anonymous code endpoint returns 401, and the data omit bias scores, embeddings, regressions, and annotation rows. The metric depends on translated stereotype lexicons, one encoder, and an extreme sentence; mean aggregation reverses direction relative to max-abs for 12.8% of stories. Regression reporting omits a complete coefficient table, errors, exact p-values, R-squared, and diagnostics, while Table 18 runs 216 uncorrected t-tests. The faithful conclusion is that trait descriptions are associated with shifts in one stereotype-alignment metric under this factorial prompt design; the study does not establish that Dark Triad is universally more biasing than HEXACO, that prosocial traits generally attenuate bias, or that language, culture, grammar, or model scale causes the differences.
Research question
How are HEXACO and Dark Triad descriptions associated with the direction and magnitude of a gender stereotype measure in professional artifacts generated by six LLMs in English and Hindi, and how do they interact with gender, occupation, language, and model?