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        <title>BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization</title>
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            <title>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)</title>
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            <namePart type="given">Xiyan</namePart>
            <namePart type="family">Fu</namePart>
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            <namePart type="given">Eve</namePart>
            <namePart type="family">Fleisig</namePart>
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    <abstract>Large Language Models (LLMs) have become pivotal in advancing natural language processing, yet their potential to perpetuate biases poses significant concerns. This paper introduces a new framework employing Direct Preference Optimization (DPO) to mitigate gender, racial, and religious biases in LLM-generated English text. By developing a loss function that favors less biased over biased completions, our approach cultivates a preference for respectful and non-discriminatory language in LLMs. We also contribute a manually designed dataset for training LLMs to recognize and correct biases. This dataset encompasses a diverse range of prompts paired with both biased and unbiased completions. Implementing this approach on the Microsoft Phi-2 model, we demonstrate substantial reductions in biased outputs as our model outperforms the baseline model on almost all bias benchmarks. Our model also achieves better performance compared to other open-source models on most benchmarks. By reducing biases in the language generated by the model, our study marks a significant step towards developing more ethical and socially responsible LLMs. We publicly release BiasDPO dataset on HuggingFace.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-srw.7</identifier>
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%0 Conference Proceedings
%T BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization
%A Allam, Ahmed
%Y Fu, Xiyan
%Y Fleisig, Eve
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F allam-2024-biasdpo
%X Large Language Models (LLMs) have become pivotal in advancing natural language processing, yet their potential to perpetuate biases poses significant concerns. This paper introduces a new framework employing Direct Preference Optimization (DPO) to mitigate gender, racial, and religious biases in LLM-generated English text. By developing a loss function that favors less biased over biased completions, our approach cultivates a preference for respectful and non-discriminatory language in LLMs. We also contribute a manually designed dataset for training LLMs to recognize and correct biases. This dataset encompasses a diverse range of prompts paired with both biased and unbiased completions. Implementing this approach on the Microsoft Phi-2 model, we demonstrate substantial reductions in biased outputs as our model outperforms the baseline model on almost all bias benchmarks. Our model also achieves better performance compared to other open-source models on most benchmarks. By reducing biases in the language generated by the model, our study marks a significant step towards developing more ethical and socially responsible LLMs. We publicly release BiasDPO dataset on HuggingFace.
%R 10.18653/v1/2024.acl-srw.7
%U https://aclanthology.org/2024.acl-srw.7
%U https://doi.org/10.18653/v1/2024.acl-srw.7
%P 42-50

Markdown (Informal)

[BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization](https://aclanthology.org/2024.acl-srw.7) (Allam, ACL 2024)

• BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization (Allam, ACL 2024)

ACL

• Ahmed Allam. 2024. BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 42–50, Bangkok, Thailand. Association for Computational Linguistics.