# LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels

arXiv: https://arxiv.org/abs/2510.14640 · HTML: https://mlsanity.com/paper/2510.14640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [Large Language Models Enable Few-Shot Clustering](https://mlsanity.com/paper/2307.00524/) (2023)
- [ClusterLLM: Large Language Models as a Guide for Text Clustering](https://mlsanity.com/paper/2305.14871/) (2023)
- [IDAS: Intent Discovery with Abstractive Summarization](https://mlsanity.com/paper/2305.19783/) (2023)
- [User Intent Prediction in Information-seeking Conversations](https://mlsanity.com/paper/1901.03489/) (2019)
- [Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11](https://mlsanity.com/paper/2304.12982/) (2023)
- [IntentGPT: Few-shot Intent Discovery with Large Language Models](https://mlsanity.com/paper/2411.10670/) (2024)
- [A Survey of Ontology Expansion for Conversational Understanding](https://mlsanity.com/paper/2410.15019/) (2024)
- [SPILL: Domain-Adaptive Intent Clustering based on Selection and Pooling with Large Language Models](https://mlsanity.com/paper/2503.15351/) (2025)
- [In a Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification](https://mlsanity.com/paper/2504.21398/) (2025)

## Abstract (arXiv)

In this paper, we propose an intuitive, training-free and label-free method for intent clustering in conversational search. Current approaches to short text clustering use LLM-generated pseudo-labels to enrich text representations or to identify similar text pairs for pooling. The limitations are: (1) each text is assigned only a single label, and refining representations toward a single label can be unstable; (2) text-level similarity is treated as a binary selection, which fails to account for continuous degrees of similarity. Our method LUMI is designed to amplify similarities between texts by using shared pseudo-labels. We first generate pseudo-labels for each text and collect them into a pseudo-label set. Next, we compute the mean of the pseudo-label embeddings and pool it with the text embedding. Finally, we perform text-level pooling: Each text representation is pooled with its similar pairs, where similarity is determined by the degree of shared labels. Our evaluation on four benchmark sets shows that our approach achieves competitive results, better than recent state-of-the-art baselines, while avoiding the need to estimate the number of clusters during embedding refinement, as is required by most methods. Our findings indicate that LUMI can effectively be applied in unsupervised short-text clustering scenarios.