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The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA
May 3, 2024, 4:15 a.m. | Lee Youngmin, Lang S. I. D. Andrew, Cai Duoduo, Wheat R. Stephen
cs.CL updates on arXiv.org arxiv.org
Abstract: This study introduces a systematic framework to compare the efficacy of Large Language Models (LLMs) for fine-tuning across various cheminformatics tasks. Employing a uniform training methodology, we assessed three well-known models-RoBERTa, BART, and LLaMA-on their ability to predict molecular properties using the Simplified Molecular Input Line Entry System (SMILES) as a universal molecular representation format. Our comparative analysis involved pre-training 18 configurations of these models, with varying parameter sizes and dataset scales, followed by fine-tuning …
abstract architecture arxiv bart cs.cl cs.lg fine-tuning framework insights language language models large language large language models llama llms methodology physics.chem-ph q-bio.bm roberta role scale study tasks training type uniform
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