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        <title>Bridging Distribution Gap via Semantic Rewriting with LLMs to Enhance OOD Robustness</title>
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            <namePart type="given">Xiyan</namePart>
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    <abstract>This paper investigates the robustness of Large Language Models (LLMs) against Out-Of-Distribution (OOD) data within the context of sentiment analysis. Traditional fine-tuning approaches often fail to generalize effectively across different data distributions, limiting the practical deployment of LLMs in dynamic real-world scenarios. To address this challenge, we introduce a novel method called “Semantic Rewriting,” which leverages the inherent flexibility of LLMs to align both in-distribution (ID) and OOD data with the LLMs distributions. By semantically transforming sentences to minimize linguistic discrepancies, our approach helps to standardize features across datasets, thus enhancing model robustness. We conduct extensive experiments with several benchmark datasets and LLMs to validate the efficacy of our method. The results demonstrate that Semantic Rewriting significantly improves the performance of models on OOD tasks, outperforming traditional methods in both robustness and generalization capabilities. Our findings suggest that Semantic Rewriting is a promising technique for developing more reliable and versatile NLP systems capable of performing robustly across diverse operational environments.</abstract>
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%0 Conference Proceedings
%T Bridging Distribution Gap via Semantic Rewriting with LLMs to Enhance OOD Robustness
%A Madine, Manas
%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 madine-2024-bridging
%X This paper investigates the robustness of Large Language Models (LLMs) against Out-Of-Distribution (OOD) data within the context of sentiment analysis. Traditional fine-tuning approaches often fail to generalize effectively across different data distributions, limiting the practical deployment of LLMs in dynamic real-world scenarios. To address this challenge, we introduce a novel method called “Semantic Rewriting,” which leverages the inherent flexibility of LLMs to align both in-distribution (ID) and OOD data with the LLMs distributions. By semantically transforming sentences to minimize linguistic discrepancies, our approach helps to standardize features across datasets, thus enhancing model robustness. We conduct extensive experiments with several benchmark datasets and LLMs to validate the efficacy of our method. The results demonstrate that Semantic Rewriting significantly improves the performance of models on OOD tasks, outperforming traditional methods in both robustness and generalization capabilities. Our findings suggest that Semantic Rewriting is a promising technique for developing more reliable and versatile NLP systems capable of performing robustly across diverse operational environments.
%U https://aclanthology.org/2024.acl-srw.39
%P 458-468

Markdown (Informal)

[Bridging Distribution Gap via Semantic Rewriting with LLMs to Enhance OOD Robustness](https://aclanthology.org/2024.acl-srw.39) (Madine, ACL 2024)

• Bridging Distribution Gap via Semantic Rewriting with LLMs to Enhance OOD Robustness (Madine, ACL 2024)

ACL

• Manas Madine. 2024. Bridging Distribution Gap via Semantic Rewriting with LLMs to Enhance OOD Robustness. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 458–468, Bangkok, Thailand. Association for Computational Linguistics.