# Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigation

arXiv: https://arxiv.org/abs/2604.23600 · HTML: https://mlsanity.com/paper/2604.23600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 26 Apr 2026.

## Builds on (most cited first, 17 of 17)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [On Measuring Social Biases in Sentence Encoders](https://mlsanity.com/paper/1903.10561/) (2019)
- [Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs](https://mlsanity.com/paper/2311.04892/) (2023)
- [Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias](https://mlsanity.com/paper/2212.10678/) (2022)
- [Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community](https://mlsanity.com/paper/1603.08832/) (2016)
- [Unsupervised Discovery of Gendered Language through Latent-Variable Modeling](https://mlsanity.com/paper/1906.04760/) (2019)
- [Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology](https://mlsanity.com/paper/2405.06346/) (2024)
- [L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERT](https://mlsanity.com/paper/2304.11434/) (2023)
- [LMLPA: Language Model Linguistic Personality Assessment](https://mlsanity.com/paper/2410.17632/) (2024)
- ['Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs](https://mlsanity.com/paper/2409.13484/) (2024)
- [Mitigating Gender Stereotypes in Hindi and Marathi](https://mlsanity.com/paper/2205.05901/) (2022)
- [Investigating Gender Bias in LLM-Generated Stories via Psychological Stereotypes](https://mlsanity.com/paper/2508.03292/) (2025)
- [Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Bias](https://mlsanity.com/paper/2502.10577/) (2025)

## Built on by (most cited first, 1 of 1)

- [Anchoring LLM Gender Bias to Human Baselines: A Cross-Lingual Audit](https://mlsanity.com/paper/2605.30804/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where models are prompted to adopt specific personas when interacting with users. While persona conditioning can improve user experience and engagement, it also raises concerns about how personality cues may interact with gender biases and stereotypes. In this work, we present a controlled study of persona-conditioned story generation in English and Hindi, where each story portrays a working professional in India producing context-specific artifacts (e.g., lesson plans, reports, letters) under systematically varied persona gender, occupational role, and personality traits from the HEXACO and Dark Triad frameworks. Across 23,400 generated stories from six state-of-the-art LLMs, we find that personality traits are significantly associated with both the magnitude and direction of gender bias. In particular, Dark Triad personality traits are consistently associated with higher gender-stereotypical representations compared to socially desirable HEXACO traits, though these associations vary across models and languages. Our findings demonstrate that gender bias in LLMs is not static but context-dependent. This suggests that persona-conditioned systems used in real-world applications may introduce uneven representational harms, reinforcing gender stereotypes in generated educational, professional, or social content.