Narrative Sharpens Gender Gaps: Surveying Film Characters with LLM Agents
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Mainstream film is one of the richest sources of cultural content that AI systems learn from. Yet we have few tools for measuring the gender values it encodes. We present a proof-of-concept framework that turns fictional film characters into surveyable LLM agents. Using 160 U.S. films (1990--2019), we build 734 character agents from script dialogue and scene descriptions, condense their personas via expert-style reflections, and simulate World Values Survey gender-attitude responses. Agents reproduce systematic gender differences without explicit demographic prompting, suggesting attitudes emerge from behavior rather than identity labels. Benchmarked against historical survey data, agents exaggerate gender gaps and show greater decade-to-decade volatility than real populations. Narrative sharpens rather than homogenizes gender contrasts, complicating the consistent-input assumption underlying cultivation theory's mainstreaming mechanism. AI systems trained on such corpora may inherit this stylization before any model-level amplification occurs.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 6 of its 19 references that are in the map. First posted on arXiv 21 May 2026.
Builds on
6 of 19 references.
References · 6
- 2023Whose Opinions Do Language Models Reflect?cited by 759
- 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
- 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
- 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
- 2018The Geometry of Culture: Analyzing Meaning through Word Embeddingscited by 87
- 2024MovieSum: An Abstractive Summarization Dataset for Movie Screenplayscited by 8
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship
- 2025More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
- 2026Gender Dynamics and Homophily in a Social Network of LLM Agents
- 2026Operationalizing Perceptions of Agent Gender: Foundations and Guidelines
- 2025Automated Evaluation of Gender Bias Across 13 Large Multimodal Models
- 2026Newer Is Not Fairer: Gender Stereotyping in Text-to-Image AI Across Model Generations
- 2026The Failure Happens Before the Drift: The Social Dynamics of Values in LLM Agent Societies
- 2025Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs