1. AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy
- Author
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Pan, Rui, Nguyen, Tuan Dung, Arora, Hardik, Accomazzi, Alberto, Ghosal, Tirthankar, and Ting, Yuan-Sen
- Subjects
Astrophysics - Instrumentation and Methods for Astrophysics ,Computer Science - Computation and Language - Abstract
Continual pretraining of large language models on domain-specific data has been proposed to enhance performance on downstream tasks. In astronomy, the previous absence of astronomy-focused benchmarks has hindered objective evaluation of these specialized LLM models. Leveraging a recent initiative to curate high-quality astronomical MCQs, this study aims to quantitatively assess specialized LLMs in astronomy. We find that the previously released AstroLLaMA series, based on LLaMA-2-7B, underperforms compared to the base model. We demonstrate that this performance degradation can be partially mitigated by utilizing high-quality data for continual pretraining, such as summarized text from arXiv. Despite the observed catastrophic forgetting in smaller models, our results indicate that continual pretraining on the 70B model can yield significant improvements. However, the current supervised fine-tuning dataset still constrains the performance of instruct models. In conjunction with this study, we introduce a new set of models, AstroLLaMA-3-8B and AstroLLaMA-2-70B, building upon the previous AstroLLaMA series., Comment: 10 pages, 1 figure, 1 table, accepted to AI4S: The 5th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC24). Models will be released at https://huggingface.co/AstroMLab. AstroMLab homepage: https://astromlab.org/
- Published
- 2024