Summary of L+m-24: Building a Dataset For Language + Molecules @ Acl 2024, by Carl Edwards and Qingyun Wang and Lawrence Zhao and Heng Ji
L+M-24: Building a Dataset for Language + Molecules @ ACL 2024
by Carl Edwards, Qingyun Wang, Lawrence Zhao, Heng Ji
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM)
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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 novel dataset for training language-molecule models, which have shown great potential in molecular discovery and understanding. The existing datasets are limited by being either small and scraped from databases or large but noisy and constructed through entity linking on scientific literature. In contrast, the proposed dataset is designed to leverage the benefits of natural language in molecule design, including compositionality, functionality, and abstraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special set of data for machines that can understand both languages and molecules. Right now, there are only small datasets available or big ones that might have mistakes because they were made by connecting words to things from the past. The new dataset is designed to help with three important things about using language in molecule design: being able to combine things, making sure it works right, and being able to think about it in different ways. |
Keywords
» Artificial intelligence » Entity linking
