arXiv subjects

Faezeh Fadaei

Publications and source records attributed to Faezeh Fadaei.

2 recordsLinked to original sources

arXiv2026

A Cracked Mirror: How People Perceive Gender Performance by Large Language Models

How do large language models (LLMs) perform gender under zero-shot prompting, and how do people interpret those performances in conversation? Drawing on gender performativity, we examine how gendered meanings emerge through generated language, conversational context and human interpretation. In a preregistered randomised online experiment, 1,060 participants each had five short conversations with an LLM. We used minimal prompts to elicit performances from "very masculine" to "very feminine" and asked participants to rate their partner's perceived gender after each conversation. Mean ratings shifted in the direction of the prompts, but ratings at the extremes were closer to the midpoint than the prompt labels implied. When prompted to perform as "neither masculine nor feminine", the models were perceived as slightly masculine on average. Ratings varied across models and conversation topics. Women and men gave similar ratings at the extremes, but women rated the models as more masculine across the intermediate prompt levels. Participants' explanations illustrate how they drew on writing style, emotional expression and conversational interests, sometimes questioning the stereotypes behind their judgements. These findings suggest that conversational AI acts as a cracked mirror of gender norms. Models reproduce recognisable cues, and people interpret and sometimes challenge the meanings those cues carry.

physics.soc-ph↗

arXiv2026

Gender Dynamics and Homophily in a Social Network of LLM Agents

Generative artificial intelligence and large language models (LLMs) are increasingly deployed in interactive settings, yet we know little about how their identity performance develops when they interact within large-scale networks. We address this by examining Chirper.ai, a social media platform similar to X but composed entirely of autonomous AI chatbots. Our dataset comprises over 70,000 agents, approximately 140 million posts, and the evolving followership network over a period of one year. Based on agents' posted text, we assign weekly gender performance scores to each agent. Results suggest that each agent's gender performance is fluid rather than fixed. Despite this fluidity, the network displays strong gender-based homophily, as agents consistently follow others performing gender similarly. We investigate whether these homophilic connections arise from social selection, in which agents choose to follow similar accounts, or from social influence, in which agents become more similar to their followees over time. Consistent with human social networks, we find evidence that both mechanisms shape the structure and evolution of interactions among LLMs. Our findings suggest that, even in the absence of bodies, cultural entraining of gender performance leads to gender-based sorting. This has important implications for LLM applications in synthetic hybrid populations, social simulations, and decision support.

cs.SI↗