Summary of Chain-of-thoughts For Molecular Understanding, by Yunhui Jang et al.
Chain-of-Thoughts for Molecular Understanding
by Yunhui Jang, Jaehyung Kim, Sungsoo Ahn
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The adaptation of large language models (LLMs) to chemistry has shown promising performance, but still struggles to properly reason based on molecular structural information. To address this limitation, the authors propose StructCoT, a structure-aware chain-of-thought that enhances LLMs’ understanding of molecular structures by explicitly injecting key structural features. Two fine-tuning frameworks are introduced for adapting existing LLMs to use StructCoT, leading to consistent improvements in molecular understanding tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how big language models can be used for chemistry tasks, like describing molecules. However, these models struggle to understand the important details of a molecule’s structure. To fix this, the authors created a new way to make language models use information about a molecule’s structure when making predictions. This helps the model do better at understanding molecules and their properties. |
Keywords
» Artificial intelligence » Fine tuning
