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arXiv · 2508.03716

FeynTune: Large Language Models for High-Energy Theory

Paul Richmond·Prarit Agarwal·Borun Chowdhury·Vasilis Niarchos·Constantinos Papageorgakis
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Abstract

We present specialized Large Language Models for theoretical High-Energy Physics, obtained as 20 fine-tuned variants of the 8-billion parameter Llama-3.1 model. Each variant was trained on arXiv abstracts (through August 2024) from different combinations of hep-th, hep-ph and gr-qc. For a comparative study, we also trained models on datasets that contained abstracts from disparate fields such as the q-bio and cs categories. All models were fine-tuned using two distinct Low-Rank Adaptation fine-tuning approaches and varying dataset sizes, and outperformed the base model on hep-th abstract completion tasks. We compare performance against leading commercial LLMs (ChatGPT, Claude, Gemini, DeepSeek) and derive insights for further developing specialized language models for High-Energy Theoretical Physics.

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Paul Richmond, Prarit Agarwal, Borun Chowdhury, Vasilis Niarchos, Constantinos Papageorgakis. 2025-07-24. FeynTune: Large Language Models for High-Energy Theory. https://doi.org/10.1088/2632-2153%2Fae47bb

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