C hat GPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLM s (2024)

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.

## introduction large language models (llms) have made remarkable strides in resolving natural language processing (nlp) tasks in recent years. however, research has raised concerns about llm's fairness @xcite . since pre-trained language representations are derived by training on large human text corpora, they tend to reflect social issues present in the real world such as racial and gender biases @xcite , toxicity @xcite , and false information @xcite . when ai is used for applications such as supporting medical treatments, screening job applications, or predicting if a perpetrator would commit another crime, these biases can perpetuate discriminatory consequences throughout society. considerable efforts have been made in recent research to debias llms. however, with the large size of these models, social bias mitigation appears to be particularly challenging @xcite @xcite . traditional methods are computationally expensive as they often require model retraining @xcite . on top of that, retraining on limited data can lead to lowering llm's general language capabilities due to catastrophic forgetting @xcite . on the contrary, recent parameter-efficient methods @xcite @xcite offer a good alternative as they only require minor and targeted parameter adjustments. while more efficient, these approaches heavily rely on the quality of training data @xcite and may offer limited generalization, posing a challenge for comprehensive bias reduction @xcite . our approach: in this work, to bolster the robustness of light-weight debiasing, we propose a method to systematically prompt chatgpt @xcite to generate synthetic training data for llm debiasing (fig. 3 ). this is achieved using two distinct prompting strategies: targeted prompting and general prompting, complemented by an auxiliary method, loss-guided prompting. the first one is meant to debias models for a concrete category, which requires prior knowledge about the social bias to target. it generates synthetic data specifically to address a particular category of bias. general prompting, on the other hand, does not require information about the particular bias to target, instead relying on chatgpt's internal knowledge. this method generates data intended to be useful for mitigating bias across a range of categories. it has the potential to offer comprehenfigure 2 : average bias score across three bias categories and two metrics for different gpt2 family models before and after synthetic debiasing. we conducted extensive evaluations of the impact of bias mitigation using our synthetic datasets through the parameter-efficient method of adapter tuning @xcite across racial, gender, and religious bias. we also show promising results in debising models for challenging intersectional categories based on a recent biastestgpt dataset @xcite . prior work: studying and mitigating biases in llms has become increasingly important @xcite . recent efforts in language model bias mitigation include novel algorithms @xcite , leveraging pre-trained language models to generate gender variants for a given text @xcite , using unsupervised pipeline to curate and refine instances mentioning stereotypes @xcite , increased training scale @xcite @xcite , and extra prompting to suppress social bias @xcite . however, prior work found that current debiasing techniques heavily rely on templates and the quality of training data @xcite . at the same time existing datasets have been shown to exhibit issues related to data quality and reliability @xcite . these datasets are also hard to extend and their use for debiasing may lead to overfitting to particular social bias categories @xcite . moreover, large-scale training using methods that are not parameter-efficient is costly and can significantly compromise the general language capabilities of an llm @xcite , collectively making debiasing a challenging endeavor. recent work by xie and lukasiewicz (xie and lukasiewicz, 2023) evaluated parameter-efficient debiasing methods on two popular language models: bert @xcite and @xcite . three different parameter-efficient methods were evaluated against gender, racial, and religious bias, using existing datasets. we compare our results, which utilize synthetic data for bias mitigation, with their findings to highlight the enhanced debiasing capacity of our synthetic data. ## methodology we introduce several prompting strategies for synthetic data generation used for llm debiasing. ## results mitigating racial bias: with respect to bert, our results are in line with the baselines. our general debiasing achieves the best ss for stereoset and yields results comparable to others for the ss on crows-pairs, with the difference being less than 3%. in terms of language capability, our synthetic targeted approach secures the highest score on the gpt-2 model. for bert, while our approach is outperformed by the the best score under each metric is marked in bold and underscored. for the other models in the gpt-2 family, both targeted and general prompting strategies significantly mitigate bias across both metrics, achieving an average bias reduction of 5.7% for targeted debiasing and 5.4% for general debiasing. meanwhile, language ability is well-preserved: it is either slightly improved (approximately 0.5%) or minimally diminished (less than 1.3%). mitigating religious bias: as seen in for the other models in the gpt-2 family, general debiasing proves highly effective, yielding an average bias reduction of 7.2%. however, targeted debiasing is less effective, achieving an average reduction of only 0.7%. in terms of lms, it is well preserved, exhibiting a variation of only 1.0% compared to the original lms. mitigating gender bias: our approach effectively reduces gender bias ( our method is also highly effective on other models in the gpt-2 family in terms of reducing gender bias. we achieve an average reduction of 7.5% with targeted debiasing and 5.8% with general debiasing. the lms varies within a margin of 1.0% ## synthetic dataset analysis dataset similarity: a natural concern arises that chatgpt may know the test data and could merely reproduce the original test sets. to investigate, we analyzed the similarity between the generated synthetic data and the test set. we compared the original stereoset test set, the stereoset development set, a different dataset, our synthetic dataset, and another stereoset development set for various bias categories to check the uniqueness of our synthetic data. unseen biases: to further ensure our synthetic data is not overfitting to the existing datasets, we use biastestgpt @xcite , which generates varied test sentences for different social categories and attributes through chatgpt. while this dataset uses chatgpt for sentence generation, the crucial social group and attribute terms defining bias categories are taken from psychology-backed studies from @xcite we examine the biases from this work for gpt-2 and bert respectively ( ## discussion in this work, we introduced synthetic data generation via targeted and general prompting to debias large language models (llms). our findings offer several avenues for deeper exploration. ## limitations our evaluation primarily relies on benchmarks and datasets with a north american english focus, which may not fully represent global biases. additionally, the effectiveness of our debiasing might vary in tasks outside our testing scenarios. there's also a concern that chatgpt's exposure to test sets could have impacted our synthetic datasets @xcite . we investigated this possibility by checking if the synthetic data merely replicates known datasets and by experimenting with a newer dataset -biastestgpt. nevertheless, alignment with test sets may still exist. moreover, the dynamic nature of societal biases, which continually evolve, may require updates of our datasets. our focus on explicit biases may overlook subtler ones, needing further research @xcite . these factors emphasize the need for careful interpretation of our results and continuous improvement in debiasing approaches and datasets. ## conclusion this paper presents two new methods for generating synthetic data to reduce social bias in llms more efficiently: general and targeted prompting. these methods outperform the recent work using parameter-efficient debiasing in bias mitigation and training efficiency. they also preserve language model capabilities. our work highlights the potential of synthetic data in making llms fairer and suggests future research directions, including improving synthetic data generation, applying our approach to other domains such as vision, and exploring its broader applications beyond fairness.