Panda LLM: 面向开源中文指令遵循大语言模型的训练数据与评估
Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models
AI总结：
本研究围绕开源大语言模型的指令微调展开，探究训练数据的数量、质量、语言分布等因素对中英文指令微调模型性能的影响，配套定量分析完善评估，为开源中文对话模型发展提供参考，且相关资源已开源。
摘要：
本项目聚焦于通过指令微调提升开源大语言模型的性能，并对其表现提供全面评估。我们探究了数量、质量、语言分布等多种训练数据因素，对基于公开可用的中英文高质量指令数据集训练的指令微调模型性能的影响。我们的目标是通过定量分析补充评估结果，为开源对话模型的持续发展提供有价值的洞见。我们的模型、数据与代码均已公开，可供他人使用与拓展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
