Search Results for author:
Found 8 papers, 1 papers with code
Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models
Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks.
Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search
Recent advances demonstrate that increasing inference-time computation can significantly boost the reasoning capabilities of large language models (LLMs).
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
In contrast, training on Japanese text could improve question-answering tasks about Japanese knowledge and English-Japanese translation, which indicates that abilities for solving these two tasks can be regarded as \textit{Japanese abilities} for LLMs.
Agent Skill Acquisition for Large Language Models via CycleQD
In CycleQD, each task's performance metric is alternated as the quality measure while the others serve as the behavioral characteristics.
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs).
Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities
The results showed that the efficiency gained through vocabulary expansion had no negative impact on performance, except for the summarization task, and that the combined use of parallel corpora enhanced translation ability.
Building a Large Japanese Web Corpus for Large Language Models
Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR.
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Despite these efforts, such models encounter challenges such as limited multilingual capabilities, risks of catastrophic forgetting during continual pretraining, and the high costs of training models from scratch, alongside the need to align with AI safety standards and regulatory frameworks.
