Researchers have developed a new framework to enhance zero-shot classification for web content filtering by iteratively refining category definitions. This method utilizes Large Language Models (LLMs) to optimize definitions based on misclassified examples, improving accuracy without retraining the classification model. The approach was tested across thirteen state-of-the-art embedding foundation models and demonstrated consistent performance gains, highlighting the importance of definition quality in embedding-based systems. AI
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IMPACT Improves accuracy of web content classification systems by optimizing category definitions using LLMs.
RANK_REASON Academic paper introducing a novel framework for zero-shot classification.