Summary of Genetic Instruct: Scaling Up Synthetic Generation Of Coding Instructions For Large Language Models, by Somshubra Majumdar et al.
Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models
by Somshubra Majumdar, Vahid Noroozi, Sean Narenthiran, Aleksander Ficek, Jagadeesh Balam, Boris Ginsburg
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel method, Genetic-Instruct, to generate synthetic instructions that enhance the code generation capabilities of Large Language Models (LLMs). This approach aims to mitigate the challenges of creating instruction datasets for expert-dependent tasks like coding. By synthesizing data using another LLM, the authors propose a scalable solution that mimics evolutionary processes. The algorithm utilizes self-instruction to create numerous synthetic samples from a limited number of seeds. Fine-tuning multiple coding LLMs with these synthetic samples demonstrates a significant improvement in their code generation accuracy compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to make computers smarter by helping them learn to write code better. It’s hard to make instructions for computers because it requires experts, which can be expensive. The authors came up with an idea to use other computers to create fake instructions that can help train the real computers. This new approach is called Genetic-Instruct and it makes the training process faster and more effective. By using these fake instructions, the trained computers can write code better than before. |
Keywords
» Artificial intelligence » Fine tuning
