Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models

Fangkai Jiao; Bosheng Ding; Tianze Luo; Zhanfeng Mo

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arXiv · 2305.03025

Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models

Fangkai Jiao·Bosheng Ding·Tianze Luo·Zhanfeng Mo

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Abstract

This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance. We explore how various training data factors, such as quantity, quality, and linguistic distribution, influence the performance of instruction-tuned models trained on publicly accessible high-quality instruction datasets for both English and Chinese languages. Our goal is to supplement evaluation with quantitative analyses, providing valuable insights for the continued advancement of open-source chat models. Our model, data, and code are publicly available for others to use and build upon.

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Fangkai Jiao, Bosheng Ding, Tianze Luo, Zhanfeng Mo. 2023-05-04. Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models. https://arxiv.org/abs/2305.03025

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