L+M-24: Building a Dataset for Language + Molecules @ ACL 2024
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909242136461312 |
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| author | Edwards, Carl Wang, Qingyun Zhao, Lawrence Ji, Heng |
| author_facet | Edwards, Carl Wang, Qingyun Zhao, Lawrence Ji, Heng |
| contents | Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates. In this document, we detail the $\textit{L+M-24}$ dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024. In particular, $\textit{L+M-24}$ is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_00791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | L+M-24: Building a Dataset for Language + Molecules @ ACL 2024 Edwards, Carl Wang, Qingyun Zhao, Lawrence Ji, Heng Computation and Language Artificial Intelligence Biomolecules Quantitative Methods Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates. In this document, we detail the $\textit{L+M-24}$ dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024. In particular, $\textit{L+M-24}$ is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction. |
| title | L+M-24: Building a Dataset for Language + Molecules @ ACL 2024 |
| topic | Computation and Language Artificial Intelligence Biomolecules Quantitative Methods |
| url | https://arxiv.org/abs/2403.00791 |