从头开始合成数据：面向语言模型的通用指导调整

摘要

我们介绍了广义指令调整（称为GLAN），这是一种用于大型语言模型（LLMs）指令调整的通用且可扩展的方法。与先前依赖种子示例或现有数据集构建指令调整数据的工作不同，GLAN 专门利用预先策划的人类知识和能力分类法作为输入，并在所有领域生成大规模合成指令数据。具体来说，受人类教育系统中的系统结构启发，我们通过LLMs的协助，将人类知识和能力分解为各种领域、子领域，最终是不同学科，半自动地构建了分类法。随后，我们为每个学科生成了一个全面的科目列表，并继续设计了针对每个科目量身定制的教学大纲，再次利用LLMs。通过大纲中每节课详细的细粒度关键概念，我们能够生成涵盖人类知识和技能整个领域的多样指令。对大型语言模型（例如Mistral）的广泛实验表明，GLAN在数学推理、编码、学术考试、逻辑推理以及一般指令遵循等多个维度上表现出色，而无需使用这些任务的特定训练数据。此外，GLAN支持轻松定制，只需将新节点纳入我们的分类法即可添加新领域或技能。

English

We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on seed examples or existing datasets to construct instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of human knowledge and capabilities as input and generates large-scale synthetic instruction data across all disciplines. Specifically, inspired by the systematic structure in human education system, we build the taxonomy by decomposing human knowledge and capabilities to various fields, sub-fields and ultimately, distinct disciplines semi-automatically, facilitated by LLMs. Subsequently, we generate a comprehensive list of subjects for every discipline and proceed to design a syllabus tailored to each subject, again utilizing LLMs. With the fine-grained key concepts detailed in every class session of the syllabus, we are able to generate diverse instructions with a broad coverage across the entire spectrum of human knowledge and skills. Extensive experiments on large language models (e.g., Mistral) demonstrate that GLAN excels in multiple dimensions from mathematical reasoning, coding, academic exams, logical reasoning to general instruction following without using task-specific training data of these tasks. In addition, GLAN allows for easy customization and new fields or skills can be added by simply incorporating a new node into our taxonomy.

从头开始合成数据：面向语言模型的通用指导调整

Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

摘要

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