Back to search

Article

Emergence of Biological Structural Discovery in General-Purpose Language Models

2026-01-08

Abstract excerpt

<title>Abstract</title> <p>Large language models (LLMs) are evolving into engines for scientific discovery, yet the assumption that biological understanding requires domain-specific pre-training remains unchallenged. Here, we report that general-purpose LLMs possess an emergent capability for biological structural discovery. First, we demonstrate that a small-scale GPT-2, fine-tuned solely on English paraphrasing...

Topics

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

Identifiers and source

Literature Corpus work
18b42076-a7c5-5179-8322-f7a0cc0922cb
DOI
10.21203/rs.3.rs-8507849/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.
Emergence of Biological Structural Discovery in General-Purpose Language ModelsDOI 10.21203/rs.3.rs-8507849/v1
Select a neighboring publication to make it the new centre.