5 papers
Computed, not Memorized: Bit-Factorized Identifiers for Efficient Generative Retrieval
I-Fan Lin, Zhen Zhang, Faegheh Hasibi +1
Generative retrieval is known for its efficient end-to-end indexing and retrieval. In particular, numeric Document Identifiers (DocIDs) are generally more efficient because they ar…
LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels
I-Fan Lin, Faegheh Hasibi, Suzan Verberne
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-gene…
LLMs Enable Bag-of-Texts Representations for Short-Text Clustering
I-Fan Lin, Faegheh Hasibi, Suzan Verberne
In this paper, we propose a training-free method for unsupervised short text clustering that relies less on careful selection of embedders than other methods. In customer-facing ch…
SPILL: Domain-Adaptive Intent Clustering based on Selection and Pooling with Large Language Models
I-Fan Lin, Faegheh Hasibi, Suzan Verberne
In this paper, we propose Selection and Pooling with Large Language Models (SPILL), an intuitive and domain-adaptive method for intent clustering without fine-tuning. Existing embe…
Generate then Refine: Data Augmentation for Zero-shot Intent Detection
I-Fan Lin, Faegheh Hasibi, Suzan Verberne
In this short paper we propose a data augmentation method for intent detection in zero-resource domains. Existing data augmentation methods rely on few labelled examples for each i…