Large Language Models -- the Future of Fundamental Physics?
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918374642024448 |
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| author | Heneka, Caroline Nieser, Florian Ore, Ayodele Plehn, Tilman Schiller, Daniel |
| author_facet | Heneka, Caroline Nieser, Florian Ore, Ayodele Plehn, Tilman Schiller, Daniel |
| contents | For many fundamental physics applications, transformers, as the state of the art in learning complex correlations, benefit from pretraining on quasi-out-of-domain data. The obvious question is whether we can exploit Large Language Models, requiring proper out-of-domain transfer learning. We show how the Qwen2.5 LLM can be used to analyze and generate SKA data, specifically 3D maps of the cosmological large-scale structure for a large part of the observable Universe. We combine the LLM with connector networks and show, for cosmological parameter regression and lightcone generation, that this Lightcone LLM (L3M) with Qwen2.5 weights outperforms standard initialization and compares favorably with dedicated networks of matching size. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Models -- the Future of Fundamental Physics? Heneka, Caroline Nieser, Florian Ore, Ayodele Plehn, Tilman Schiller, Daniel Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology Data Analysis, Statistics and Probability For many fundamental physics applications, transformers, as the state of the art in learning complex correlations, benefit from pretraining on quasi-out-of-domain data. The obvious question is whether we can exploit Large Language Models, requiring proper out-of-domain transfer learning. We show how the Qwen2.5 LLM can be used to analyze and generate SKA data, specifically 3D maps of the cosmological large-scale structure for a large part of the observable Universe. We combine the LLM with connector networks and show, for cosmological parameter regression and lightcone generation, that this Lightcone LLM (L3M) with Qwen2.5 weights outperforms standard initialization and compares favorably with dedicated networks of matching size. |
| title | Large Language Models -- the Future of Fundamental Physics? |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2506.14757 |