PEFTD ebias : Capturing debiasing information using PEFT s (2023)

Abstract: The increasing use of foundation models highlights the urgent need to address and eliminate implicit biases present in them that arise during pretraining. In this paper, we introduce PEFTDebias, a novel approach that employs parameter-efficient fine-tuning (PEFT) to mitigate the biases within foundation models. PEFTDebias consists of two main phases: an upstream phase for acquiring debiasing parameters along a specific bias axis, and a downstream phase where these parameters are incorporated into the model and frozen during the fine-tuning process. By evaluating on four datasets across two bias axes namely gender and race, we find that downstream biases can be effectively reduced with PEFTs. In addition, we show that these parameters possess axis-specific debiasing characteristics, enabling their effective transferability in mitigating biases in various downstream tasks.

## introduction in recent years, it has become evident that foundation models such as bert or gpt-3 @xcite are susceptible to a range of stereotypical societal biases @xcite ) such as sexism (gender) @xcite and racism (race) @xcite , that are present in the training data. such bias axes can lead to unfair or discriminatory outcomes @xcite in various socio-technical scenarios. recent research @xcite suggests that biases acquired during pre-training can propagate to downstream models, resulting in superficial text dependencies and potential implicit bias, and a debias method to debias the fine-tuned model, which consists of two main phases -upstream phase where debiasing parameters are acquired through cda-based peft training on axis corpora, evaluated using intrinsic metrics, downstream phase, where the debiased peft is injected into a trainable model and kept frozen during the fine-tuning process on a task corpora. bias is measured using extrinsic metrics along the same axis. previous studies have attempted to address this issue by first debiasing the model and then finetuning it for a specific task. this process referred to as upstream debiasing by @xcite , and entails fine-tuning the model on upstream tasks while incorporating bias-attribute annotations for debiasing. subsequently, the model is fine-tuned for the target downstream task. nevertheless, this approach possesses certain limitations: (i) it requires annotated bias attributes for the upstream task as well as supervised data for both tasks and (ii) there is no guarantee that the model will exhibit reduced bias in the downstream task @xcite . this uncertainty arises due to the fact that modifying all parameters of the debiased upstream model might result in the loss of debiased representations. this phenomenon is commonly referred to as fairness forgetting @xcite . inspired by the promising outcomes of peft methods, which effectively capture debias information and yield competitive results compared to full model-tuning @xcite , we hypothesize that employing pefts for debiasing on an upstream bias axis could be a viable approach to mitigate bias in a foundation model for any downstream task on the same bias axis. to address this, we present a novel method called peftdebias. this approach utilizes pefts to capture debiasing information by training the model on axis-specific data during the upstream stage. subsequently, in the downstream task, the model is fine-tuned while keeping the pefts frozen, thereby preserving the upstream debiasing information along that axis. our contribution can be summarized as: several debiasing methods have been proposed in conjunction with the downstream task, including counterfactual data augmentation @xcite , dropout regularization @xcite , null-space projection @xcite , adversarial training @xcite , contrastive learning @xcite . however, these techniques necessitate expensive additional annotation, such as the inclusion of protected attributes, along with the task data. conversely, @xcite demonstrate debiasing using only task data, showing its potential for improving generalization. in contrast, @xcite indicate that debiasing a language model (lm) prior to fine-tuning does not guarantee unbiasedness in the resulting fine-tuned model. @xcite investigate the transferability of debiasing techniques. they begin by applying bias mitigation to a pre-trained model through fine-tuning and subsequently employ it for downstream fine-tuning. @xcite ; @xcite show that peft methods like adapters @xcite , can be used to debias language models (lms) while keeping the lm backbone frozen. @xcite present a method to do debiasining by identifying sparse subnetworks that correspond to different bias axes, which can subsequently be composed. a notable advantage of these approaches is the reduced computational cost and environmental impact associated with debiasing lms @xcite . additionally, it holds the potential for preventing catastrophic forgetting of pre-trained knowledge caused by finetuning @xcite . however, these techniques are typically applied during the downstream phase and possess the limitations discussed earlier. ## bias factors and datasets we validate our hypothesis by conducting validation on two widely recognized factors of social bias: gender stereotyping and racial identifiers. to address occupation-based gender stereotypes, we utilize the biasbios dataset @xcite . for the bias related to race, we address the issue of elevated occurrences of false positive outcomes in hate speech predictions using ghc @xcite . to show our generalizibility of capturing debiasing information along a specific axis using pefts, we show transfer to datasets mnli (multi genre nli) @xcite and lhc (large hate corpus) @xcite along gender and race axis respectively. in order to assess the effectiveness of our debi-asing techniques in mitigating gender and racial biases, we utilize two intrinsic bias benchmarks, namely crows-pairs @xcite and stereoset @xcite , during the initial phase of our evaluation, referred to as the upstream stage. stereoset evaluates a language model's stereotypical associations by employing fill-in-the-blank problems with intra-sentence examples across different bias categories. crows-pairs is an intra-sentence dataset of minimal pairs that compares the language model's masked token probabilities of sentences with disadvantaged or advantaged races fulfilling or violating stereotypes. in the subsequent downstream stage, we evaluate the performance gap of pefts across different protected attributes within the specific domain using extrinsic bias metrics. to measure gender bias, we adopt the method proposed by @xcite to calculate the gender gap in the true positive rate (tpr) for each occupation (tpr-gap). to assess racial bias, we compute the false positive rate difference (fprd) by comparing the fpr of examples mentioning protected racial attributes to the overall fpr. we calculate fprd for both the in-domain data and the identity phrase templates test sets (iptts) @xcite , which consist of 77k instances. these instances comprise hate and non-hate sentences that mention 25 racial identifiers and are generated using predefined templates. to measure transferability, we evaluate mnli using fn (fraction of neutrals) in bias-nli @xcite , a nli dataset to measure gender bias, and lhc using iptts. ## results our experimental setup is described in a.4. we present three sets of results: evaluation of the upstream and downstream phases on the same datasets, and the transferability to other datasets. ## conclusion & future work this research paper introduces peftdebias, a novel debiasing approach that utilizes pefts to mitigate the biases. peftdebias involves two phases: an upstream phase for learning debiasing pefts along specific bias axes, and a downstream phase where these pefts are incorporated into the model and kept frozen while fine-tuning. experimental results highlight the effectiveness of prompt tuning for downstream debiasing and the transferability of axis-specific debiasing parameters in mitigating biases across different tasks. future work includes extending our technique for generative models and tasks, as well as exploring the composition of multiple bias axes @xcite to address various biases in datasets.