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        <title>Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering</title>
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        <namePart type="given">Arijit</namePart>
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    <abstract>Robustness in Natural Language Processing continues to be a pertinent issue, where state of the art models under-perform under naturally shifted distributions. In the context of Question Answering, work on domain adaptation methods continues to be a growing body of research. However, very little attention has been given to the notion of domain generalization under natural distribution shifts, where the target domain is unknown. With drastic improvements in the quality and access to generative models, we answer the question: How do generated datasets influence the performance of QA models under natural distribution shifts? We perform experiments on 4 different datasets under varying amounts of distribution shift, and analyze how “in-the-wild” generation can help achieve domain generalization. We take a two-step generation approach, generating both contexts and QA pairs to augment existing datasets. Through our experiments, we demonstrate how augmenting reading comprehension datasets with generated data leads to better robustness towards natural distribution shifts.</abstract>
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%0 Conference Proceedings
%T Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering
%A Chowdhury, Arijit
%A Chadha, Aman
%Y Falk, Neele
%Y Papi, Sara
%Y Zhang, Mike
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F chowdhury-chadha-2024-generative
%X Robustness in Natural Language Processing continues to be a pertinent issue, where state of the art models under-perform under naturally shifted distributions. In the context of Question Answering, work on domain adaptation methods continues to be a growing body of research. However, very little attention has been given to the notion of domain generalization under natural distribution shifts, where the target domain is unknown. With drastic improvements in the quality and access to generative models, we answer the question: How do generated datasets influence the performance of QA models under natural distribution shifts? We perform experiments on 4 different datasets under varying amounts of distribution shift, and analyze how “in-the-wild” generation can help achieve domain generalization. We take a two-step generation approach, generating both contexts and QA pairs to augment existing datasets. Through our experiments, we demonstrate how augmenting reading comprehension datasets with generated data leads to better robustness towards natural distribution shifts.
%U https://aclanthology.org/2024.eacl-srw.20
%P 258-265

Markdown (Informal)

[Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering](https://aclanthology.org/2024.eacl-srw.20) (Chowdhury & Chadha, EACL 2024)

• Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering (Chowdhury & Chadha, EACL 2024)

ACL

• Arijit Chowdhury and Aman Chadha. 2024. Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 258–265, St. Julian’s, Malta. Association for Computational Linguistics.