bibtext: @inproceedings{
  han2024chatgpt,
  title={Chat{GPT} Based Data Augmentation for Improved Parameter-Efficient Debiasing of {LLM}s},
  author={Pengrui Han and Rafal Dariusz Kocielnik and Adhithya Prakash Saravanan and Roy Luoyao Jiang and Or Sharir and Anima Anandkumar},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=q5Ft9ZJtHm}
  }
abstract: Large Language models (LLMs), while powerful, exhibit harmful social biases. Debiasing is often challenging due to computational costs, data constraints, and potential degradation of multi-task language capabilities. This work introduces a novel approach utilizing ChatGPT to generate synthetic training data, aiming to enhance the debiasing of LLMs. We propose two strategies: Targeted Prompting, which provides effective debiasing for known biases but necessitates prior specification of bias in question; and General Prompting, which, while slightly less effective, offers debiasing across various categories. We leverage resource-efficient LLM debiasing using adapter tuning and compare the effectiveness of our synthetic data to existing debiasing datasets. Our results reveal that: (1) ChatGPT can efficiently produce high-quality training data for debiasing other LLMs; (2) data produced via our approach surpasses existing datasets in debiasing performance while also preserving internal knowledge of a pre-trained LLM; and (3) synthetic data exhibits generalizability across categories, effectively mitigating various biases, including intersectional ones. These findings underscore the potential of synthetic data in advancing the fairness of LLMs with minimal retraining cost.
title: ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs
authors: Pengrui Han Rafal Dariusz Kocielnik Adhithya Prakash Saravanan Roy Luoyao Jiang Or Sharir Anima Anandkumar