Summary of Llasmol: Advancing Large Language Models For Chemistry with a Large-scale, Comprehensive, High-quality Instruction Tuning Dataset, by Botao Yu et al.
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
by Botao Yu, Frazier N. Baker, Ziqi Chen, Xia Ning, Huan Sun
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a breakthrough in using large language models (LLMs) for chemistry tasks. Despite LLMs like GPT-4 showing impressive results on natural language processing, they struggled with chemistry-related tasks. The authors propose SMolInstruct, a comprehensive dataset of 14 selected chemistry tasks and over three million samples, which outperforms existing GPT-4 models by a significant margin. By fine-tuning open-source LLMs using SMolInstruct, the researchers identify Mistral as the best base model for chemistry tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chemistry is important in many areas like medicine and materials science. But current language models are not very good at understanding chemistry concepts. This paper shows that a new kind of language model can do much better on chemistry tasks than existing ones. They created a big dataset with lots of examples of chemistry problems to help the model learn. With this training, the model became really good at solving chemistry problems. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Language model » Natural language processing
