Framework acquires descriptive text for entities via web and LLMs to train classifiers from names and labels alone, achieving 82.3% and 72.9% macro F1 on SIC code and healthcare taxonomy classification tasks.
It handles the entire process through a novel text acquisi- tion method that leverages web retrieval and LLM-based generation to produce descriptive text for classifier training
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Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks
Framework acquires descriptive text for entities via web and LLMs to train classifiers from names and labels alone, achieving 82.3% and 72.9% macro F1 on SIC code and healthcare taxonomy classification tasks.