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        <title>Revisiting Relation Extraction in the era of Large Language Models</title>
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        <namePart type="given">Somin</namePart>
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    <abstract>Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a sequence-to-sequence task, linearizing relations between entities as target strings to be generated conditioned on the input. Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision. We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the few-shot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results. We release this model as a new baseline for RE tasks.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-long.868</identifier>
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%0 Conference Proceedings
%T Revisiting Relation Extraction in the era of Large Language Models
%A Wadhwa, Somin
%A Amir, Silvio
%A Wallace, Byron
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wadhwa-etal-2023-revisiting
%X Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a sequence-to-sequence task, linearizing relations between entities as target strings to be generated conditioned on the input. Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision. We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the few-shot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results. We release this model as a new baseline for RE tasks.
%R 10.18653/v1/2023.acl-long.868
%U https://aclanthology.org/2023.acl-long.868
%U https://doi.org/10.18653/v1/2023.acl-long.868
%P 15566-15589

Markdown (Informal)

[Revisiting Relation Extraction in the era of Large Language Models](https://aclanthology.org/2023.acl-long.868) (Wadhwa et al., ACL 2023)

• Revisiting Relation Extraction in the era of Large Language Models (Wadhwa et al., ACL 2023)

ACL

• Somin Wadhwa, Silvio Amir, and Byron Wallace. 2023. Revisiting Relation Extraction in the era of Large Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15566–15589, Toronto, Canada. Association for Computational Linguistics.