Summary of Benchmarking Large Language Models For Molecule Prediction Tasks, by Zhiqiang Zhong and Kuangyu Zhou and Davide Mottin
Benchmarking Large Language Models for Molecule Prediction Tasks
by Zhiqiang Zhong, Kuangyu Zhou, Davide Mottin
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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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 investigates the potential of Large Language Models (LLMs) in molecule prediction tasks, a crucial area where structured data like graphs pose significant challenges. The researchers question whether LLMs can effectively handle such tasks and, instead of focusing on top-tier performance, aim to explore how they can contribute to diverse molecule tasks. They design prompts for LLMs to query six standard molecule datasets and compare their performance with existing Machine Learning (ML) models, including text-based models and those specifically designed for analyzing molecular structure. The results reveal that LLMs generally lag behind ML models but show promise in enhancing their performance when used collaboratively. This study highlights the limitations of LLMs in comprehending graph data and identifies challenges and potential avenues to harness them for molecule prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) can predict molecules, which is important for fields like biology and chemistry. LLMs struggle with structured data like graphs, and they’re not as good as other models designed specifically for analyzing molecular structure. The researchers asked LLMs questions about molecule tasks and compared their answers to those of other models. They found that LLMs aren’t the best at this task yet, but they can help improve the performance of other models when used together. This study helps us understand what LLMs are good or bad at and how we can use them better in the future. |
Keywords
* Artificial intelligence * Machine learning
