Year: 2025
Type: preprint
Abstract: This study presents an analysis of modern open-source large language models (LLMs) -- including Llama, Qwen, and Gemma -- to evaluate their encoded knowledge of Quantum Chromodynamics (QCD). Through reverse engineering of these models' representations, we uncover the naturally idiosyncratic patterns in how foundational QCD concepts are embedded within their parameter spaces. Our methodology combines targeted probing techniques and knowledge extraction protocols to assess the models' understanding of critical QCD principles like color confinement, asymptotic freedom, and the running coupling constant. This work provides a tool for utilizing LLMs as an assistant in physics research, while also highlighting current limitations in their representation of advanced quantum field theory concepts that future model development should address.
Source: arXiv (Cornell University)
Authors: Sulc, Antonin, Connor, Patrick L. S.
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416999536
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