Related papers: Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes

Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes

URL: http://arxiv.org/abs/2402.01981v1
Date: Sat, 3 Feb 2024 01:40:11 GMT
Title: Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes
Authors: Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Tong Yu, Hanieh Deilamsalehy, Ruiyi Zhang, Sungchul Kim, Franck Dernoncourt
Abstract summary: We leverage the zero-shot capabilities of large language models to reduce stereotyping. We show that self-debiasing can significantly reduce the degree of stereotyping across nine different social groups. We hope this work opens inquiry into other zero-shot techniques for bias mitigation.
Score: 73.12947922129261
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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