Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models

TL;DR

Genetic-Instruct is a scalable evolutionary algorithm that synthesizes millions of high-quality coding instructions for large language models, significantly enhancing their code generation abilities.

Contribution

It introduces a novel, parallelizable method for generating large-scale, high-quality coding instructions using LLMs and evolutionary principles, even with limited seed data.

Findings

Generated over 7.5 million coding instructions

Fine-tuning LLMs with synthetic data improves code generation

Outperforms existing synthetic data approaches

Abstract

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.