Ziya2: A Data-Centric Approach to Enhance Large Language Models' Capabilities in Chinese, Mathematics, and Programming
Ziya2, a 13-billion-parameter language model, is developed through a data-centric approach that focuses on optimizing the use of pre-training data to enhance the model's capabilities in Chinese, mathematics, and programming tasks, while maintaining or improving its performance on general English benchmarks.
Enhancing Large Language Models through Adaptive Data Engineering: The LLM-ADE Framework
The LLM-ADE framework introduces a novel approach to continual pre-training of large language models, enabling efficient integration of new datasets while mitigating catastrophic forgetting and double descent.
Enhancing Llama-3 Language Model with Optimal Continual Pre-Training on Additional Chinese Corpus
Continual pre-training of the Llama-3 language model with an optimal mixture ratio of additional Chinese corpus can substantially improve its performance on Chinese-related tasks as well as certain domain-specific capabilities like math, coding, and emotional intelligence.
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