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arXiv AI By Jayasimha Talur, Oleg Smirnov, Paul Missault

Few-Shot Out of Domain Intent Detection with Covariance Corrected Mahalanobis Distance

Read the original on arXiv AI →

The paper introduces a covariance‑corrected Mahalanobis distance method for detecting out‑of‑domain (OOD) intents in conversational agents. It builds on prior work showing Mahalanobis distance’s effectiveness but addresses its shortcomings in few‑shot scenarios, where limited examples of new intents are available. The authors analyze why the original approach underperforms and propose a refined distance metric to improve OOD detection in practical settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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