# ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs

arXiv: https://arxiv.org/abs/2402.11764 · HTML: https://mlsanity.com/paper/2402.11764/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2024.

## Built on by (most cited first, 18 of 18)

- [The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs](https://mlsanity.com/paper/2509.03730/) (2025)
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- [Are Large Language Models a Good Replacement of Taxonomies?](https://mlsanity.com/paper/2406.11131/) (2024)
- [Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective](https://mlsanity.com/paper/2506.05166/) (2025)
- [A Multi-LLM Debiasing Framework](https://mlsanity.com/paper/2409.13884/) (2024)
- [FusionFactory: Fusing LLM Capabilities with Multi-LLM Log Data](https://mlsanity.com/paper/2507.10540/) (2025)
- ['Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs](https://mlsanity.com/paper/2409.13484/) (2024)
- [In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Models](https://mlsanity.com/paper/2409.15454/) (2024)
- [Steer2Adapt: Dynamically Composing Steering Vectors Elicits Efficient Adaptation of LLMs](https://mlsanity.com/paper/2602.07276/) (2026)
- [Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewriting](https://mlsanity.com/paper/2503.06781/) (2025)
- [KLAAD: Refining Attention Mechanisms to Reduce Societal Bias in Generative Language Models](https://mlsanity.com/paper/2507.19962/) (2025)
- [BiasCause: Evaluate Socially Biased Causal Reasoning of Large Language Models](https://mlsanity.com/paper/2504.07997/) (2025)
- [SocialVeil: Probing Social Intelligence of Language Agents under Communication Barriers](https://mlsanity.com/paper/2602.05115/) (2026)
- [Self-Anchored Attention Model for Sample-Efficient Classification of Prosocial Text Chat](https://mlsanity.com/paper/2506.09259/) (2025)
- [Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior](https://mlsanity.com/paper/2606.12730/) (2026)
- [A Heuristic Perspective on Debiasing Language Models](https://mlsanity.com/paper/2608.00622/) (2026)
- [Context-Aware Counterfactual Data Augmentation for Gender Bias Mitigation in Language Models](https://mlsanity.com/paper/2602.09590/) (2026)
- [Acmite: Mitigating Gender Bias in LLMs through Concept-Guided Mutual Information](https://mlsanity.com/paper/2610.01696/) (2026)

## Abstract (arXiv)

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.