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        <title>Large Language Models for Scientific Information Extraction: An Empirical Study for Virology</title>
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    <abstract>In this paper, we champion the use of structured and semantic content representation of discourse-based scholarly communication, inspired by tools like Wikipedia infoboxes or structured Amazon product descriptions. These representations provide users with a concise overview, aiding scientists in navigating the dense academic landscape. Our novel automated approach leverages the robust text generation capabilities of LLMs to produce structured scholarly contribution summaries, offering both a practical solution and insights into LLMs’ emergent abilities.For LLMs, the prime focus is on improving their general intelligence as conversational agents. We argue that these models can also be applied effectively in information extraction (IE), specifically in complex IE tasks within terse domains like Science. This paradigm shift replaces the traditional modular, pipelined machine learning approach with a simpler objective expressed through instructions. Our results show that finetuned FLAN-T5 with 1000x fewer parameters than the state-of-the-art GPT-davinci is competitive for the task.</abstract>
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%0 Conference Proceedings
%T Large Language Models for Scientific Information Extraction: An Empirical Study for Virology
%A Shamsabadi, Mahsa
%A D’Souza, Jennifer
%A Auer, Sören
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F shamsabadi-etal-2024-large
%X In this paper, we champion the use of structured and semantic content representation of discourse-based scholarly communication, inspired by tools like Wikipedia infoboxes or structured Amazon product descriptions. These representations provide users with a concise overview, aiding scientists in navigating the dense academic landscape. Our novel automated approach leverages the robust text generation capabilities of LLMs to produce structured scholarly contribution summaries, offering both a practical solution and insights into LLMs’ emergent abilities.For LLMs, the prime focus is on improving their general intelligence as conversational agents. We argue that these models can also be applied effectively in information extraction (IE), specifically in complex IE tasks within terse domains like Science. This paradigm shift replaces the traditional modular, pipelined machine learning approach with a simpler objective expressed through instructions. Our results show that finetuned FLAN-T5 with 1000x fewer parameters than the state-of-the-art GPT-davinci is competitive for the task.
%U https://aclanthology.org/2024.findings-eacl.26
%P 374-392

Markdown (Informal)

[Large Language Models for Scientific Information Extraction: An Empirical Study for Virology](https://aclanthology.org/2024.findings-eacl.26) (Shamsabadi et al., Findings 2024)

• Large Language Models for Scientific Information Extraction: An Empirical Study for Virology (Shamsabadi et al., Findings 2024)

ACL

• Mahsa Shamsabadi, Jennifer D’Souza, and Sören Auer. 2024. Large Language Models for Scientific Information Extraction: An Empirical Study for Virology. In Findings of the Association for Computational Linguistics: EACL 2024, pages 374–392, St. Julian’s, Malta. Association for Computational Linguistics.