Large Language Models for Scientific Information Extraction: An Empirical Study for Virology

TL;DR

This study explores using large language models to automatically generate structured summaries of scientific contributions in virology, demonstrating their effectiveness in complex information extraction tasks with fewer parameters.

Contribution

The paper introduces a novel automated method leveraging LLMs for structured scientific content summarization, highlighting their emergent abilities in complex information extraction within scientific domains.

Findings

Finetuned FLAN-T5 achieves competitive results with significantly fewer parameters.

Structured content representation aids in navigating dense scientific literature.

LLMs can effectively replace traditional modular information extraction pipelines.

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.