5 citations · 5 across the 6 of their papers we have counts for
10 papers · 1 filter
A Survey on Recent Advances in Conversational Data Generation
Heydar Soudani, Roxana Petcu, Evangelos Kanoulas +1
Recent advancements in conversational systems have significantly enhanced human-machine interactions across various domains. However, training these systems is challenging due to t…
Reproducing Complex Set-Compositional Information Retrieval
Vincent Degenhart, Dewi Timman, Arjen P. de Vries +2
Complex information needs may involve set-compositional queries using conjunction, disjunction, and exclusion, yet it remains unclear whether current retrieval paradigms genuinely…
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…
PromptAug: Fine-grained Conflict Classification Using Data Augmentation
Oliver Warke, Joemon M. Jose, Faegheh Hasibi +1
Given the rise of conflicts on social media, effective classification models to detect harmful behaviours are essential. Following the garbage-in-garbage-out maxim, machine learnin…
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…