Automatic Optimization and Evaluation of Prompt Fairness Based on Large Language Model Itself

With the rapid development of large language models,the issue of model fairness has garnered increasing attention,primarily focusing on biases in generated text and downstream tasks.To produce fairer text,careful design and examination of the fairness of prompts are necessary.This study employs four Chinese large language models as optimizers to automatically and ite-ratively generate fair prompts that describe both advantaged and disadvantaged groups.Additionally,it investigates the impact of variables such as model temperature,initial prompt types,and optimized directions on the optimization process,while assessing the fairness of various prompt styles,including chain-of-thought and persona.The results indicate that large language models can effectively generate prompts that are either less biased or more biased,with prompts for advantaged groups performing better at lower temperature settings.Generating biased prompts is relatively more challenging,with the models employing anti-adversarial strategies to tackle this task.Using questions as initial prompts can yield outputs that are more random yet of higher quality.Different models exhibit distinct optimization strategies,with chain-of-thought and debiasing styles producing fairer text.Prompts play a crucial role in model fairness and warrant further investigation into their fairness.