用任务原则生成数据，小模型也能高效训练，省碳又防泄露
Evolution without Large Models: Training Language Model with Task Principles
- 先让大模型提炼任务原则，再由小模型依据原则生成数据
- 小模型性能接近使用大模型直接生成数据的效果
- 减少碳排放，避免闭源模型数据泄露，适合资源受限场景
主流语言模型训练依赖大模型扩增人工数据集，虽降低标注成本，但仍面临数据增强时碳排放高及使用闭源大模型导致的数据泄露风险。为此，我们提出一种自进化方法：首先通过多层级原则生成，让大模型基于少量任务数据总结出任务完成原则；随后利用这些原则，由小规模语言模型生成大量训练数据。实验表明，该方法显著提升小模型性能，优于直接使用小模型生成数据的方式。由于仅在生成原则阶段使用大模型，整体训练碳排放大幅降低。
原文摘要 · Abstract (English)
A common training approach for language models involves using a large-scale language model to expand a human-provided dataset, which is subsequently used for model training.This method significantly reduces training costs by eliminating the need for extensive human data annotation. However, it still faces challenges such as high carbon emissions during data augmentation and the risk of data leakage when we use closed-source LLMs. To address these issues, we propose a self-evolution method for language models. First, we introduce the Multi-level Principle Generation, which enables a large-scale model to summarize task-completion principles based on a small amount of task data. Then, we propose the Principle-based Instance Generation, in which a smaller-scale language model uses these task principles to generate a large amount of data. This data is then used for model training. Experimental results show that our proposed method significantly improves model performance compared to directly using a smaller-scale language model to generate data. Additionally, since we only use the large-scale language model to generate the task-completion principles, the carbon emissions associated with training the model are greatly reduced.
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