2 citations · 3 across the 3 of their papers we have counts for
7 papers · 1 filter
Compressing Language Models for Specialized Domains
Miles Williams, George Chrysostomou, Vitor Jeronymo +1
Language models (LMs) excel at tasks across diverse domains, yet require substantial computational resources during inference. Compression techniques such as pruning and quantizati…
Self-calibration for Language Model Quantization and Pruning
Miles Williams, George Chrysostomou, Nikolaos Aletras
Quantization and pruning are fundamental approaches for model compression, enabling efficient inference for language models. In a post-training setting, state-of-the-art quantizati…
Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
George Chrysostomou, Zhixue Zhao, Miles Williams +1
Despite the remarkable performance of generative large language models (LLMs) on abstractive summarization, they face two significant challenges: their considerable size and tenden…
On the Impact of Temporal Concept Drift on Model Explanations
Zhixue Zhao, George Chrysostomou, Kalina Bontcheva +1
Explanation faithfulness of model predictions in natural language processing is typically evaluated on held-out data from the same temporal distribution as the training data (i.e.…
An Empirical Study on Explanations in Out-of-Domain Settings
George Chrysostomou, Nikolaos Aletras
Recent work in Natural Language Processing has focused on developing approaches that extract faithful explanations, either via identifying the most important tokens in the input (i…
Frustratingly Simple Pretraining Alternatives to Masked Language Modeling
Atsuki Yamaguchi, George Chrysostomou, Katerina Margatina +1
Masked language modeling (MLM), a self-supervised pretraining objective, is widely used in natural language processing for learning text representations. MLM trains a model to pred…