Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911788241518592 |
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| author | Fang, Yin Liang, Xiaozhuan Zhang, Ningyu Liu, Kangwei Huang, Rui Chen, Zhuo Fan, Xiaohui Chen, Huajun |
| author_facet | Fang, Yin Liang, Xiaozhuan Zhang, Ningyu Liu, Kangwei Huang, Rui Chen, Zhuo Fan, Xiaohui Chen, Huajun |
| contents | Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_08018 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models Fang, Yin Liang, Xiaozhuan Zhang, Ningyu Liu, Kangwei Huang, Rui Chen, Zhuo Fan, Xiaohui Chen, Huajun Quantitative Methods Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Information Retrieval Machine Learning Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability. |
| title | Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models |
| topic | Quantitative Methods Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2306.08018 |