10 citations · 13 across the 5 of their papers we have counts for
5 papers
Visual-Word Tokenizer: Beyond Fixed Sets of Tokens in Vision Transformers
Leonidas Gee, Wing Yan Li, Viktoriia Sharmanska +1
The cost of deploying vision transformers increasingly represents a barrier to wider industrial adoption. Existing compression techniques require additional end-to-end fine-tuning…
Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency
Leonidas Gee, Milan Gritta, Gerasimos Lampouras +1
Code Language Models have been trained to generate accurate solutions, typically with no regard for runtime. On the other hand, previous works that explored execution optimisation…
Are Compressed Language Models Less Subgroup Robust?
Leonidas Gee, Andrea Zugarini, Novi Quadrianto
To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to…
Fast Vocabulary Transfer for Language Model Compression
Leonidas Gee, Andrea Zugarini, Leonardo Rigutini +1
Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer.…
Multi-word Tokenization for Sequence Compression
Leonidas Gee, Leonardo Rigutini, Marco Ernandes +1
Large Language Models have proven highly successful at modelling a variety of tasks. However, this comes at a steep computational cost that hinders wider industrial uptake. In this…