On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023)

Abstract: While understanding and removing gender biases in language models has been a long-standing problem in Natural Language Processing, prior research work has primarily been limited to English. In this work, we investigate some of the challenges with evaluating and mitigating biases in multilingual settings which stem from a lack of existing benchmarks and resources for bias evaluation beyond English especially for non-western context. In this paper, we first create a benchmark for evaluating gender biases in pre-trained masked language models by extending DisCo to different Indian languages using human annotations. We extend various debiasing methods to work beyond English and evaluate their effectiveness for SOTA massively multilingual models on our proposed metric. Overall, our work highlights the challenges that arise while studying social biases in multilingual settings and provides resources as well as mitigation techniques to take a step toward scaling to more languages.

## introduction large language models (llms) @xcite @xcite have obtained impressive performance on a wide range of nlp tasks showing great potential in several downstream applications for real world impact. however, these models have shown to be prone to picking up unwanted correlations and stereotypes from the pre-training data @xcite @xcite which, can perpetuate harmful biases for people belonging to marginalized groups. while there has been a great deal of interest in understanding and mitigating such biases in llms @xcite @xcite , the focus of such studies has primarily been on english. while massively multilingual language models @xcite ; xue * equal contribution @xcite , have shown impressive performances across a wide range of languages, especially with their surprising effectiveness at zero-shot crosslingual transfer, there still exists a lack of focused research to evaluate and mitigate the biases that exist in these models. this can lead to a lack of inclusive and responsible technologies for groups whose native language is not english and can also lead to the dissemination of stereotypes and the widening of existing cultural gaps. past work on evaluating and mitigating biases in multilingual models has mostly been concerned with gender bias in cross-lingual word embeddings @xcite which fails to account for contextual information @xcite , making them unreliable for llms. other methods for estimating biases in contextualized representations involve multilingual bias evaluation @xcite , which utilizes parallel translation corpora in different languages that might lack non-western cultural contexts @xcite . for debiasing llms, @xcite proposed an adapter @xcite based approach. however, the biases are measured in the word representations and only english data was used for debiasing, missing out on cultural context for other languages. to address these concerns, we make the following key contributions in our work. first, we extend the disco metric @xcite ) by creating human-corrected templates for 6 indian languages. disco takes sentence-level context while measuring bias and our templates are largely culturally agnostic making them more generally applicable. second, we extend existing debiasing strategies like counterfactual data augmentation @xcite and self-debiasing @xcite to mitigate gender biases across languages in masked language models (mlms). finally, we also evaluate the transferability of debiasing mlms from one source language to other target languages and observe limited transfer from english to languages lacking western context. however, we do observe that typologically and culturally similar languages aid each other in reducing gender bias. while there have been multiple studies on measuring biases in multilingual models, previous work has not explored mitigating gender biases from these models on multiple languages and studying the transferability of debiasing across different languages. this is especially true while using nonembedding based approaches for evaluation and debiasing. to the best of our knowledge, ours is the first work to debias multilingual llms for different languages and measure the cross-lingual transfer for gender bias mitigation. to encourage future research in this area, we will release our code and datasets publically 1 . ## measuring bias in multilingual models in this section, we describe the benchmarks to evaluate biases in mlms across different languages. since most existing benchmarks for bias evaluation in contextualized representations are designed for english, we discuss our multilingual variant of disco and the recently proposed mbe metric. ## mitigating bias in multilingual models we next discuss how we extend bias mitigation techniques to work beyond english along with different fine-tuning and prompting strategies that we deploy in our experiments. ## results we evaluate the out of box (oob) biases as well the effect of applying aforementioned debiasing techniques in multilingual mlms like xlmr-base @xcite , indicbert @xcite , and mbert (cased) @xcite using our multilingual disco metric. additionally, we also evaluate language-specific monolingual models (refer comparison between different fine-tuning setups for cda: we first compare the results of bias mitigation across all 4 classes of finetuning setups for cda to understand the effect each had on the final bias reduction. as can be seen in comparison between cda and self-debiasing: counter to cda, self-debiasing shows different bias mitigation trends for indian languages. evaluation on mbe metric: we first investigate the effect of self-debiasing on monolingual models when evaluated for the mbe metric. as can be observed in figure 2a , for most languages (except russian and spanish), both variants of self-debiasing manage to reduce the biases substantially. however, when we compare the results on a multilingual model i.e. xlmr in figure 2b , we again observe the same phenomenon as for multilingual disco, where the biases tend to increase upon applying self-debiasing. figure 2a shows that sden and sd-l have similar debiasing performance for monolingual models. it is intriguing that monolingual models are able to debias so well based on english prompts. this similarity in results with non-english and english prompts could possibly be explained by contamination in the pretraining monolingual data @xcite . we also compare the effect of cda-{en}on reducing the biases and we observed it does obtain more success in most languages (except spanish and japanese). even though mbe and multilingual disco have different experimental setups, obtaining consistent results while using the two different metrics like english-only debiasing being insufficient to reduce biases in other languages. selfdebiasing being ineffective for mitigating biases in multilingual models strenghtens the applicability of our results. our results indicate that self-debiasing might be limited for multilingual models and we leave the investigation of this phenomenon to future work. ## conclusion in this work, we investigated gender biases in multilingual settings by proposing a bias evaluation dataset in 6 indian languages. we further extended debiasing approaches like cda and self-debiasing to work for languages beyond english and evaluated their effectiveness in removing biases across languages in mlms. one of our key findings is that debiasing with english data might only provide a limited bias reduction in other languages and even collecting a limited amount of counterfactual data through translation can lead to substantial improvements when jointly trained with such data from similar languages. finally, we showed that despite being effective on monolingual models, self-debiasing is limited in reducing biases in mul-tilingual models with often resulting in an increase in overall bias. we hope that our work will act as a useful resource for the community to build more inclusive technologies for all cultures.