Summary of Large Language Models Are In-context Molecule Learners, by Jiatong Li et al.
Large Language Models are In-Context Molecule Learners
by Jiatong Li, Wei Liu, Zhihao Ding, Wenqi Fan, Yuqiang Li, Qing Li
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 This paper proposes In-Context Molecule Adaptation (ICMA), a novel approach for Large Language Models (LLMs) to learn the alignment between molecular and textual spaces without requiring extra domain-specific pre-training stages. ICMA consists of three stages: Hybrid Context Retrieval, Post-retrieval Re-ranking, and In-context Molecule Tuning. The first stage retrieves informative context examples using BM25 Caption Retrieval and Molecule Graph Retrieval. Then, the second stage further improves the quality of retrieval results with Sequence Reversal and Random Walk. Finally, the third stage adapts the parameters of LLMs for the molecule-caption translation task using retrieved examples. Experimental results demonstrate that ICMA can empower LLMs to achieve state-of-the-art or comparable performance without extra training corpora and intricate structures, showing that LLMs are inherently in-context molecule learners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand molecules better by teaching them how to translate text into molecular information. Currently, machines need to learn from lots of specific data before they can do this task well. This new approach, called In-Context Molecule Adaptation (ICMA), lets machines learn to do this task without needing all that extra training. ICMA works by first finding the right information and then using it to make the machine better at translating text into molecules. The results show that this approach can make machines really good at doing this task, which is important for many scientific and medical applications. |
Keywords
» Artificial intelligence » Alignment » Translation
