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Teaching LLMs to Speak Spectroscopy
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Teaching LLMs to Speak Spectroscopy
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Pre-trained Large Language Models (LLMs) have revolutionized text processing, yet adapting Transformer-based neural networks to non-textual scientific modalities typically requires specialized architectures and extensive computational resources. We demonstrate that LLaMA-3.1-8B can be efficiently repurposed to predict galaxy redshifts from spectroscopic data through Low-Rank Adaptation (LoRA), achieving competitive performance while preserving its linguistic capabilities. Using only 16 GPU-hours and adapting 0.04% of model parameters, our approach achieves a mean absolute error of 0.04 in redshift prediction while retaining over 85% of performance on AstroBench and 89% on general QA tasks from eval-harness. This minimal-effort adaptation--requiring only simple standard fine-tuning APIs--lowers barriers to entry for domain scientists and enables integrated agentic workflows where a single model handles both spectroscopic data for quantitative analysis and natural language for reasoning.
Forward citations
Cited by 4 Pith papers
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Executable verification through formalized expert reasoning in astronomical spectroscopy
An LLM-based multi-agent system (FORMA) verifies DESI spectral classifications with 95.5% agreement to expert adjudication by reconstructing expert reasoning into an auditable workflow.
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Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference
Two-stage LLM framework infers stellar parameters and ~20 elemental abundances from spectra, showing performance gains with increasing data volume.
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Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference
A two-stage LLM framework infers stellar parameters and ~20 elemental abundances from spectra, with performance improving as training data increases.
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Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference
A two-stage LLM framework infers stellar parameters and ~20 elemental abundances from spectra, with performance improving systematically as training data volume increases.
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