UDRIL routes only high-uncertainty user queries to a fine-tuned LLM, improving out-of-scope intent detection while keeping most predictions on a cheap classifier.
NNK-Means: Data summarization using dictionary learning with non-negative kernel regression
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abstract
An increasing number of systems are being designed by gathering significant amounts of data and then optimizing the system parameters directly using the obtained data. Often this is done without analyzing the dataset structure. As task complexity, data size, and parameters all increase to millions or even billions, data summarization is becoming a major challenge. In this work, we investigate data summarization via dictionary learning~(DL), leveraging the properties of recently introduced non-negative kernel regression (NNK) graphs. Our proposed NNK-Means, unlike previous DL techniques, such as kSVD, learns geometric dictionaries with atoms that are representative of the input data space. Experiments show that summarization using NNK-Means can provide better class separation compared to linear and kernel versions of kMeans and kSVD. Moreover, NNK-Means is scalable, with runtime complexity similar to that of kMeans.
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cs.CL 1years
2025 1verdicts
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Efficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing
UDRIL routes only high-uncertainty user queries to a fine-tuned LLM, improving out-of-scope intent detection while keeping most predictions on a cheap classifier.