遗传指令：扩展大型语言模型编码指令的合成生成规模
Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models
AI总结：
提出Genetic-Instruct，一种基于进化原理的可扩展算法，从种子指令合成大规模高质量编码指令，通过三个LLM协作生成，微调后显著提升代码生成能力。
摘要：
大型语言模型（LLMs）需要高质量的指令数据以实现有效对齐，特别是在代码生成任务中，专家策划的数据集生成成本高昂。我们提出Genetic-Instruct，一种利用进化原理合成大规模高质量编码指令的可扩展算法。从一小部分种子指令出发，Genetic-Instruct通过利用Instructor-LLM进行生成、Coder-LLM进行代码合成以及Judge-LLM进行自动质量评估，生成多样且具有挑战性的指令-代码对。我们提出的方法高度可并行化，即使使用少量种子数据和较弱的生成器模型也有效。我们使用该方法生成了超过750万条编码指令。随后，我们通过使用合成样本微调LLMs进行评估，并展示了与其他合成生成方法和公开数据集相比，其代码生成能力的显著提升。我们的结果突出了Genetic-Instruct框架的效率、可扩展性和泛化性。
英文摘要：
Large Language Models (LLMs) require high quality instruction data for effective alignment, particularly in code generation tasks where expert curated datasets are expensive to produce. We present Genetic-Instruct, a scalable algorithm for synthesizing large-scale, high quality coding instructions using evolutionary principles. Starting from a small set of seed instructions, Genetic-Instruct generates diverse and challenging instruction-code pairs by leveraging an Instructor-LLM for generation, a Coder-LLM for code synthesis, and a Judge-LLM for automatic quality evaluation. Our proposed approach is highly parallelizable and effective even with a small seed data and weaker generator models. We generated more than 7.5 million coding instructions with the proposed approach. Then we evaluated it by fine-tuning LLMs with the synthetic samples and demonstrated a significant improvement in their code generation capability compared to the other synthetic generation approaches and publicly available datasets. Our results highlight the efficiency, scalability, and generalizability of the Genetic-Instruct framework.
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。
