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LLM-assisted clustering framework improves intent discovery in dialogue systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed NILC, a new framework designed to improve new intent discovery in dialogue systems. NILC iteratively refines text embeddings and cluster centroids using large language models (LLMs) to better capture nuanced semantic meanings. The framework also incorporates LLM-generated semantic centroids and augments ambiguous utterances to enhance cluster accuracy, showing significant performance gains across various datasets in both unsupervised and semi-supervised settings. AI

IMPACT Enhances dialogue system capabilities by improving the accuracy of recognizing user intents.

RANK_REASON The cluster contains an academic paper detailing a new method for intent discovery using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-assisted clustering framework improves intent discovery in dialogue systems

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The cluster contains an academic paper detailing a new method for intent discovery using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hongtao Wang, Renchi Yang, Wenqing Lin ·

    NILC: Discovering New Intents with LLM-assisted Clustering

    arXiv:2511.05913v2 Announce Type: replace-cross Abstract: New intent discovery (NID) seeks to recognize both new and known intents from unlabeled user utterances, which finds prevalent use in practical dialogue systems. Existing works towards NID mainly adopt a cascaded architect…

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