Self-Debiasing Large Language Models: Zero-Shot Recognition and
  Reduction of Stereotypes
Isabel O. Gallegos Ryan A. Rossi Joe Barrow Md Mehrab Tanjim Tong Yu Hanieh Deilamsalehy Ruiyi Zhang Sungchul Kim Franck Dernoncourt
Large language models (LLMs) have shown remarkable advances in language
generation and understanding but are also prone to exhibiting harmful social
biases. While recognition of these behaviors has generated an abundance of bias
mitigation techniques, most require modifications to the training data, model
parameters, or decoding strategy, which may be infeasible without access to a
trainable model. In this work, we leverage the zero-shot capabilities of LLMs
to reduce stereotyping in a technique we introduce as zero-shot self-debiasing.
With two approaches, self-debiasing via explanation and self-debiasing via
reprompting, we show that self-debiasing can significantly reduce the degree of
stereotyping across nine different social groups while relying only on the LLM
itself and a simple prompt, with explanations correctly identifying invalid
assumptions and reprompting delivering the greatest reductions in bias. We hope
this work opens inquiry into other zero-shot techniques for bias mitigation.