6 papers
A Function-Centric Perspective on Flat and Sharp Minima
Israel Mason-Williams, Gabryel Mason-Williams, Helen Yannakoudakis
Flat minima are strongly associated with improved generalisation in deep neural networks. However, this connection has proven nuanced in recent studies, with both theoretical count…
A Functional Perspective on Knowledge Distillation in Neural Networks
Israel Mason-Williams, Gabryel Mason-Williams, Helen Yannakoudakis
Knowledge distillation is considered a compression mechanism when judged on the resulting student's accuracy and loss, yet its functional impact is poorly understood. We quantify t…
KidsArtBench: Multi-Dimensional Children's Art Evaluation with Attribute-Aware MLLMs
Mingrui Ye, Chanjin Zheng, Zengyi Yu +4
Multimodal Large Language Models (MLLMs) show remarkable progress across many visual-language tasks; however, their capacity to evaluate artistic expression remains limited. Aesthe…
A Survey of Cognitive Distortion Detection and Classification in NLP
Archie Sage, Jeroen Keppens, Helen Yannakoudakis
As interest grows in applying natural language processing (NLP) techniques to mental health, an expanding body of work explores the automatic detection and classification of cognit…
Prompting open-source and commercial language models for grammatical error correction of English learner text
Christopher Davis, Andrew Caines, Ãistein Andersen +6
Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown tha…
Learning New Tasks from a Few Examples with Soft-Label Prototypes
Avyav Kumar Singh, Ekaterina Shutova, Helen Yannakoudakis
Existing approaches to few-shot learning in NLP rely on large language models (LLMs) and/or fine-tuning of these to generalise on out-of-distribution data. In this work, we propose…