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

• URL: http://arxiv.org/abs/2407.21077v1
• Date: Mon, 29 Jul 2024 20:42:59 GMT
• Title: Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models
• Authors: Somshubra Majumdar, Vahid Noroozi, Sean Narenthiran, Aleksander Ficek, Jagadeesh Balam, Boris Ginsburg,
• Abstract summary: We introduce a scalable method for generating synthetic instructions to enhance the code generation capability of Large Language Models. The proposed algorithm, Genetic-Instruct, mimics evolutionary processes, utilizing self-instruction to create numerous synthetic samples from a limited number of seeds.
• Score: 54.51932175059004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) rely on instruction samples for alignment, but creating these datasets poses challenges, particularly in expert-dependent tasks like coding, which can be cost-prohibitive. One approach to mitigate these challenges is synthesizing data using another LLM. In this paper, we introduce a scalable method for generating synthetic instructions to enhance the code generation capability of LLMs. The proposed algorithm, Genetic-Instruct, mimics evolutionary processes, utilizing self-instruction to create numerous synthetic samples from a limited number of seeds. Genetic-Instruct is designed for efficient scaling of the generation process. Fine-tuning multiple coding LLMs with the synthetic samples demonstrates a significant improvement in their code generation accuracy compared to the baselines.

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