Summary of Selfcodealign: Self-alignment For Code Generation, by Yuxiang Wei et al.
SelfCodeAlign: Self-Alignment for Code Generation
by Yuxiang Wei, Federico Cassano, Jiawei Liu, Yifeng Ding, Naman Jain, Zachary Mueller, Harm de Vries, Leandro von Werra, Arjun Guha, Lingming Zhang
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Software Engineering (cs.SE)
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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 proposed SelfCodeAlign pipeline is a supervised fine-tuning approach for large language models (LLMs) that enables them to follow human instructions more effectively. By employing the same base model throughout the data generation process, SelfCodeAlign generates new tasks by extracting diverse coding concepts from high-quality seed snippets and validates responses in a sandbox environment. This leads to a finetuned model that achieves state-of-the-art performance on HumanEval+ benchmark, surpassing larger models like CodeLlama-70B-Instruct despite being smaller. SelfCodeAlign is shown to be effective across LLMs of various sizes, from 3B to 33B, and the base models can benefit more from alignment with their own data distribution. The pipeline’s components are validated individually, demonstrating that SelfCodeAlign outperforms direct distillation from GPT-4o and leading GPT-3.5-based distillation methods. The finetuned model is used to generate a dataset of 74k instruction-response pairs, which leads to a model that achieves a 67.1 pass@1 on HumanEval+, surpassing CodeLlama-70B-Instruct despite being ten times smaller. Across all benchmarks, this finetuned model consistently outperforms the original version trained with OctoPack. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SelfCodeAlign is a new way to make large language models better at following human instructions. This approach uses the same base model throughout the process and makes sure that the generated data is high-quality and diverse. It then validates the responses in a special environment before selecting the best ones for further training. This method has been tested on different-sized models, from small to very large, and it works well across all of them. The results show that SelfCodeAlign can make smaller models perform better than larger models, which is important because bigger models are not always better. SelfCodeAlign also creates a new kind of model called StarCoder2-Instruct, which is the first model that is fully transparent, open-source, and self-aligned to achieve state-of-the-art coding performance. This means that developers can use this model without worrying about copyright or licensing issues. |
Keywords
» Artificial intelligence » Alignment » Distillation » Fine tuning » Gpt » Supervised
