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 effectively. We present a dataset-adaptive post-processing architecture developed for Customer Intent Extraction (CIE), where unstructured customer language is transformed into stable, traceable intent units. The approach separates recall-oriented extraction from utility-oriented reduction. Source-specific preprocessing first isolates evidence from multimodal plans, sparse operational records, and structured opportunity da...
| Authors | Mahesh Viswanathan, Joan Rossello, Leticia Fernandes et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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