alpaca-data-gpt4-chinese

Large Language Models (LLMs) have shown impressive generalization capabilities such as in-context-learning and chain-of-thoughts reasoning. To enable LLMs to follow natural language instructions and complete real-world tasks, researchers have been exploring methods of instruction-tuning of LLMs. To advance the state of the art of instruction-tuning for LLMs, we present the first attempt to use GPT-4 to generate instruction-following data for LLM finetuning.

大语言模型（Large Language Models, LLMs）已展现出卓越的泛化能力，例如上下文学习（in-context-learning）与思维链（chain-of-thoughts）推理。为使大语言模型能够遵循自然语言指令并完成现实世界任务，研究者们一直在探索针对大语言模型的指令微调（instruction-tuning）方法。为推动大语言模型指令微调领域的当前最优水平，本研究首次尝试利用GPT-4生成用于大语言模型微调的指令遵循数据集。

搜集汇总

背景与挑战

背景概述

该数据集由Microsoft Research在2023年发布，旨在通过GPT-4生成指令跟随数据，用于大语言模型的指令微调，以提升其遵循自然语言指令和完成实际任务的能力。