Data and Application Code for "How LLMs Perform Gender Under Zero-Shot Prompting"

This repository contains the experimental data and oTree application code associated with the study “How LLMs Perform Gender Under Zero-Shot Prompting”. The study investigates how large language models (LLMs) perform gender in text-based conversation. The repository includes: A dataset containing 5,300 observations from 1,060 participants The oTree application code used to implement the experiment and interactions with the LLMs A README file describing the dataset, variables, and application code