Learning from Red Teaming: Gender Bias Provocation and Mitigation in
  Large Language Models
Hsuan Su Cheng-Chu Cheng Hua Farn Shachi H Kumar Saurav Sahay Shang-Tse Chen Hung-yi Lee
Recently, researchers have made considerable improvements in dialogue systems
with the progress of large language models (LLMs) such as ChatGPT and GPT-4.
These LLM-based chatbots encode the potential biases while retaining
disparities that can harm humans during interactions. The traditional biases
investigation methods often rely on human-written test cases. However, these
test cases are usually expensive and limited. In this work, we propose a
first-of-its-kind method that automatically generates test cases to detect
LLMs' potential gender bias. We apply our method to three well-known LLMs and
find that the generated test cases effectively identify the presence of biases.
To address the biases identified, we propose a mitigation strategy that uses
the generated test cases as demonstrations for in-context learning to
circumvent the need for parameter fine-tuning. The experimental results show
that LLMs generate fairer responses with the proposed approach.