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Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi +1
Large language models (LLMs) are capable of solving a wide range of tasks, yet they have struggled with reasoning. To address this, we propose $\textbf{Additional Logic Training (A…
Adapting Chat Language Models Using Only Target Unlabeled Language Data
Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio +1
Vocabulary expansion (VE) is the de-facto approach to language adaptation of large language models (LLMs) by adding new tokens and continuing pre-training on target data. While thi…
How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?
Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras
Large language models (LLMs) have shown remarkable capabilities in many languages beyond English. Yet, LLMs require more inference steps when generating non-English text due to the…
An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference
Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras
The development of state-of-the-art generative large language models (LLMs) disproportionately relies on English-centric tokenizers, vocabulary and pre-training data. Despite the f…