用强模型指导弱模型，实现伦理合规的代码生成。
Applying the Chinese Wall Reverse Engineering Technique to Large Language Model Code Editing
- 强模型生成详细指令，指导弱模型完成复杂任务。
- Comma v0.1 1T 在 CanItEdit 上性能提升超66%。
- 适合关注版权合规与模型伦理的研究者和开发者。
大型代码语言模型在编程环境中应用日益广泛。尽管实用，但顶级模型的训练数据仍不公开，引发版权担忧。部分模型如 Pleias 和 Comma 强调数据筛选与许可，但因训练数据有限，性能不足，仅作为概念验证。为提升此类模型实用性，我们提出应用“中国墙”反向工程技巧——利用高质量模型生成详细指令，指导能力较弱的模型完成任务。实验表明，该方法使 Comma v0.1 1T 在 CanItEdit 基准测试中性能提升超过66%，SternCoder2 Instruct 提升约20%。然而，当前该技术实际应用受限于缺乏无版权争议的公共领域训练数据。
原文摘要 · Abstract (English)
Large language models for code (Code LLM) are increasingly utilized in programming environments. Despite their utility, the training datasets for top LLM remain undisclosed, raising concerns about potential copyright violations. Some models, such as Pleias and Comma put emphasis on data curation and licenses, however, with limited training data these models are not competitive and only serve as proof of concepts. To improve the utility of these models, we propose an application of the "Chinese Wall" technique, inspired by the reverse engineering technique of the same name -- a high quality model is used to generate detailed instructions for a weaker model. By doing so, a weaker but ethically aligned model may be used to perform complicated tasks that, otherwise, can only be completed by more powerful models. In our evaluation, we've found that this technique improves Comma v0.1 1T's performance in CanItEdit benchmark by over 66%, and Starcoder2 Instruct by roughly 20% compared to when running the same model on the benchmark alone. The practical application of this technique today, however, may be limited due to the lack of models trained on public domain content without copyright restrictions.
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