medical-6
Chemical Entity Recognition
3260 papers • 126 benchmarks • 313 datasets
Chemical Entity Recognition (CER) is a fundamental task in biomedical text mining and Natural Language Processing (NLP). It involves the identification and classification of chemical entities in textual data, such as scientific literature. These entities can encompass a broad range of concepts including chemical compounds, drugs, elements, ions or functional groups. Given the complexity and variety of chemical nomenclature, the CER task represents a significant challenge for LLMs, and their performance in this task can provide important insights into their overall capabilities in the biomedical domain.
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Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Huajun Chen, Xiaozhuan Liang, Zhuo Chen, Yin Fang, Ningyu Zhang, Kangwei Liu, Rui Huang, Xiaohui Fan•Mon Jun 12 2023
Through extensive instruction tuning experiments on LLMs, the effectiveness of Mol-Instructions is demonstrated in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolescular research community.
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