183 citations · 552 across the 27 of their papers we have counts for
37 papers
Don't Inoculate Everything: Stratified Inoculation Prompting Narrows Backdoor Triggers and Preserves Desired Traits
Kajetan Dymkiewicz, Tim Farrelly, Adam Práda +8
Supervised fine-tuning can teach language models undesired behaviours alongside desired ones. Inoculation prompting (IP) aims to limit unwanted generalisation by requesting the und…
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 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…
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 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…
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection
Ivo Verhoeven, Pushkar Mishra, Rahel Beloch +2
Community models for malicious content detection, which take into account the context from a social graph alongside the content itself, have shown remarkable performance on benchma…
Prompting open-source and commercial language models for grammatical error correction of English learner text
Christopher Davis, Andrew Caines, Øistein 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…
On the application of Large Language Models for language teaching and assessment technology
Andrew Caines, Luca Benedetto, Shiva Taslimipoor +12
The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented impact in the popular media and public consciousness, giving rise to a mixture of…
Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers
Kamil Bujel, Andrew Caines, Helen Yannakoudakis +1
Long-sequence transformers are designed to improve the representation of longer texts by language models and their performance on downstream document-level tasks. However, not much…
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension
Zhi Zhang, Helen Yannakoudakis, Xiantong Zhen +1
The task of multimodal referring expression comprehension (REC), aiming at localizing an image region described by a natural language expression, has recently received increasing a…
FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs
Niels van der Heijden, Ekaterina Shutova, Helen Yannakoudakis
We present FewShotTextGCN, a novel method designed to effectively utilize the properties of word-document graphs for improved learning in low-resource settings. We introduce K-hop…
Scientific and Creative Analogies in Pretrained Language Models
Tamara Czinczoll, Helen Yannakoudakis, Pushkar Mishra +1
This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2. Existing analogy datasets typically focus on a limited set of analogi…
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…
Ruddit: Norms of Offensiveness for English Reddit Comments
Rishav Hada, Sohi Sudhir, Pushkar Mishra +3
On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds. Automatic metho…
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability
Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova
Abuse on the Internet is an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse across various pl…
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing
Anna Langedijk, Verna Dankers, Phillip Lippe +4
Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply mo…
Zero-shot Sequence Labeling for Transformer-based Sentence Classifiers
Kamil Bujel, Helen Yannakoudakis, Marek Rei
We investigate how sentence-level transformers can be modified into effective sequence labelers at the token level without any direct supervision. Existing approaches to zero-shot…
Multilingual and cross-lingual document classification: A meta-learning approach
Niels van der Heijden, Helen Yannakoudakis, Pushkar Mishra +1
The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods. In this work, we propose a meta-learning approa…
A Multimodal Framework for the Detection of Hateful Memes
Phillip Lippe, Nithin Holla, Shantanu Chandra +4
An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes. Designing systems to automatically detect hateful content is of para…
The Teacher-Student Chatroom Corpus
Andrew Caines, Helen Yannakoudakis, Helena Edmondson +4
The Teacher-Student Chatroom Corpus (TSCC) is a collection of written conversations captured during one-to-one lessons between teachers and learners of English. The lessons took pl…
Analyzing Neural Discourse Coherence Models
Youmna Farag, Josef Valvoda, Helen Yannakoudakis +1
In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various…
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses
Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis +2
Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum…
Meta-Learning with Sparse Experience Replay for Lifelong Language Learning
Nithin Holla, Pushkar Mishra, Helen Yannakoudakis +1
Lifelong learning requires models that can continuously learn from sequential streams of data without suffering catastrophic forgetting due to shifts in data distributions. Deep le…
Graph-based Modeling of Online Communities for Fake News Detection
Shantanu Chandra, Pushkar Mishra, Helen Yannakoudakis +3
Over the past few years, there has been a substantial effort towards automated detection of fake news on social media platforms. Existing research has modeled the structure, style,…