7 citations · 17 across the 21 of their papers we have counts for
6 papers · 1 filter
Kurate: Scalable Scientific Quality Analysis
Matthew J. Vowels, Jamie Cummins
Scientific search systems can find papers that are relevant to a question, but they generally do not assess the quality of the evidence that those papers provide. We present Kurate…
K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations
Laura M. Vowels, Matthew J. Vowels, Shivali Sharma +9
People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk conversations remains poorly characterised. We developed K-B…
Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs
Alireza A. Safaei, Laura M. Vowels, Matthew J. Vowels +2
The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environmental cost. In this paper, we e…
Sequences as Nodes for Contrastive Multimodal Graph Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
To tackle cold-start and data sparsity issues in recommender systems, numerous multimodal, sequential, and contrastive techniques have been proposed. While these augmentations can…
Multimodal Enhancement of Sequential Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuS…
DSL: Understanding and Improving Softmax Recommender Systems with Competition-Aware Scaling
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
Softmax Loss (SL) is being increasingly adopted for recommender systems (RS) as it has demonstrated better performance, robustness and fairness. Yet in implicit-feedback, a single…