Large language models can pull useful signals from messy enterprise data, but doing so with high recall often produces outputs that are duplicated, inconsistent in granularity, or semantically overlapping. A new arXiv paper frames this as a mismatch between recall and utility: too many items, even if relevant, become noise for anyone trying to act on them.
The authors propose a dataset-adaptive post-processing method for customer intents generated by LLMs. Instead of applying a one-size-fits-all cleanup, the approach adjusts to the specific structure and distribution of each dataset, aiming to reduce redundancy and balance granularity while preserving the underlying intents.
The work highlights that post-processing is not a trivial deduplication step but a key part of making LLM extraction usable in real-world enterprise settings. By focusing on utility rather than raw recall, the paper offers a practical direction for deploying LLMs in customer analytics pipelines.