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arXiv — cs.AI

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From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents

Large language models can extract useful signals from heterogeneous enterprise data, but high-recall extraction often produces outputs that are duplicated, uneven in granularity, semantically overlapping, or too numerous for downstream systems and human reviewers to use…

arXiv — cs.AIBy: Mahesh Viswanathan, Joan Rossello, Leticia Fernandes, Paul MutaweOctober 8, 2026 (Oct 8)
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  • Large language models can extract useful signals from heterogeneous enterprise data, but high-recall extraction often produces outputs that are duplicated, uneven in granularity, semantically overlapping, or too numerous for downstream systems and human reviewers to use…
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From High Recall to High Utility: Dataset-Adaptive Post-Processing of LLM-Generated Customer Intents

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