10 citations · 10 across the 9 of their papers we have counts for
12 papers
Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training Debiasing
Filip Trhlik, Andrew Caines, Paula Buttery
Pre-trained language models (LMs) have, over the last few years, grown substantially in both societal adoption and training costs. This rapid growth in size has constrained progres…
Looking to Learn: Token-wise Dynamic Gating for Low-Resource Vision-Language Modelling
Bianca-Mihaela Ganescu, Suchir Salhan, Andrew Caines +1
Training vision-language models on cognitively-plausible amounts of data requires rethinking how models integrate multimodal information. Within the constraints of the Vision track…
From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes
Zébulon Goriely, Richard Diehl Martinez, Andrew Caines +2
Language models are typically trained on large corpora of text in their default orthographic form. However, this is not the only option; representing data as streams of phonemes ca…
Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing
Richard Diehl Martinez, Zebulon Goriely, Andrew Caines +2
Language models strongly rely on frequency information because they maximize the likelihood of tokens during pre-training. As a consequence, language models tend to not generalize…
Tending Towards Stability: Convergence Challenges in Small Language Models
Richard Diehl Martinez, Pietro Lesci, Paula Buttery
Increasing the number of parameters in language models is a common strategy to enhance their performance. However, smaller language models remain valuable due to their lower operat…
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…
CLIMB: Curriculum Learning for Infant-inspired Model Building
Richard Diehl Martinez, Zebulon Goriely, Hope McGovern +4
We describe our team's contribution to the STRICT-SMALL track of the BabyLM Challenge. The challenge requires training a language model from scratch using only a relatively small t…
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…
Probing for targeted syntactic knowledge through grammatical error detection
Christopher Davis, Christopher Bryant, Andrew Caines +2
Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode…
Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers
Mariano Felice, Shiva Taslimipoor, Paula Buttery
This paper presents the first multi-objective transformer model for constructing open cloze tests that exploits generation and discrimination capabilities to improve performance. O…
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…
Adaptive Forgetting Curves for Spaced Repetition Language Learning
Ahmed Zaidi, Andrew Caines, Russell Moore +2
The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the f…