Back to search

Article

Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery

2023-07-11

Abstract excerpt

<h4>Background: </h4> Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially drive a new era in biomedical research, reducing the barriers for accessing existing medical evidence. This work examines the potential of LLMs for dialoguing with biomedical background knowledge, using the context of antibiotic discovery as an exempla...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
a364402e-4800-5c6f-af80-5b391eed0431
DOI
10.21203/rs.3.rs-3117447/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discoveryDOI 10.21203/rs.3.rs-3117447/v1
Select a neighboring publication to make it the new centre.