9 papers
On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain
Atsuki Yamaguchi, Szymon Palucha, Léo Bijar +2
Mixture-of-Experts (MoE) models offer inference speedups via selective activation but impose substantial memory requirements because the whole network must remain loaded. Structure…
MultiHashFormer: Hash-based Generative Language Models
Huiyin Xue, Atsuki Yamaguchi, Nikolaos Aletras
Language models (LMs) represent tokens using embedding matrices that scale linearly with the vocabulary size. To constrain the parameter footprint, prior work proposes hashing many…
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning
Maggie Mi, Golzar Atefi, Atsuki Yamaguchi +3
Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability is thought to predict syntact…
How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data
Joel Niklaus, Atsuki Yamaguchi, Michal Štefánik +9
Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and s…
Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks
Atsuki Yamaguchi, Maggie Mi, Nikolaos Aletras
Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning of world knowledge and reasoning,…
Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates
Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio +1
Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the reliance on costly specialized target l…