AI RESEARCH
When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction
arXiv CS.LG
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ArXi:2604.19335v1 Announce Type: new The rapid growth of chemical literature has generated vast amounts of unstructured data, where reaction information is particularly valuable for applications such as reaction predictions and drug design. However, the prohibitive cost of expert annotation has led to a scarcity of