12 citations · 25 across the 9 of their papers we have counts for
17 papers · 1 filter
North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)
Tom Kocmi, Alexandre Bérard, Phil Blunsom +20
We present North Small Translate, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same foundation as Cohere's Command…
Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
Kazuki Yano, Shun Kiyono, Sosuke Kobayashi +2
We investigate the role of learning rate scheduling in the large-scale pre-training of large language models, focusing on its influence on downstream performance after supervised f…
Efficient Construction of Model Family through Progressive Training Using Model Expansion
Kazuki Yano, Sho Takase, Sosuke Kobayashi +2
As Large Language Models (LLMs) gain widespread practical application, offering model families with varying parameter sizes has become standard practice to accommodate diverse comp…
Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability
Ryokan Ri, Shun Kiyono, Sho Takase
Zero-shot cross-lingual transfer by fine-tuning multilingual pretrained models shows promise for low-resource languages, but often suffers from misalignment of internal representat…
Large Vocabulary Size Improves Large Language Models
Sho Takase, Ryokan Ri, Shun Kiyono +1
This paper empirically investigates the relationship between subword vocabulary size and the performance of large language models (LLMs) to provide insights on how to define the vo…
Spike No More: Stabilizing the Pre-training of Large Language Models
Sho Takase, Shun Kiyono, Sosuke Kobayashi +1
Loss spikes often occur during pre-training of large language models. The spikes degrade the performance of large language models and sometimes ruin the pre-training. Since the pre…