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

• URL: http://arxiv.org/abs/2407.21077v2
• Date: Mon, 07 Apr 2025 23:35:11 GMT
• Title: Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models
• Authors: Somshubra Majumdar, Vahid Noroozi, Mehrzad Samadi, Sean Narenthiran, Aleksander Ficek, Wasi Uddin Ahmad, Jocelyn Huang, Jagadeesh Balam, Boris Ginsburg,
• Abstract summary: Large Language Models (LLMs) require high quality instruction data for effective alignment.<n>We present Genetic-Instruct, a scalable algorithm for synthesizing large-scale, high quality coding instructions.
• Score: 59.60208063956459
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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