Large Language Models for Scientific Information Extraction: An
  Empirical Study for Virology
Mahsa Shamsabadi Jennifer D'Souza Sören Auer
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.