This work introduces LAB (Large-scale Alignment for chatBots), a novel methodology designed to overcome the scalability challenges in the instruction-tuning phase of large language model (LLM) training. Leveraging a taxonomy-guided synthetic data generation process and a multi-phase tuning framework, LAB significantly reduces reliance on expensive human annotations and proprietary models like GPT-4. We demonstrate that LAB-trained models can achieve competitive performance across several benchmarks compared to models trained with traditional human-annotated or GPT-4 generated synthetic data. Thus offering a scalable, cost-effective solution for enhancing LLM capabilities and instruction-following behaviors without the drawbacks of catastrophic forgetting, marking a step forward in the efficient training of LLMs for a wide range of applications.

翻译：本文提出了一种名为LAB（Large-scale Alignment for chatBots，面向聊天机器人的大规模对齐）的新颖方法，旨在克服大语言模型（LLM）训练中指令微调阶段的可扩展性挑战。通过利用基于分类学引导的合成数据生成过程和多阶段微调框架，LAB显著减少了对昂贵人工标注和GPT-4等专有模型的依赖。我们证明，与传统人工标注或GPT-4生成的合成数据训练的模型相比，LAB训练的模型能够在多个基准测试中达到具有竞争力的性能。因此，该方法提供了一种可扩展且经济高效的解决方案，用于增强LLM的能力和指令遵循行为，且不引入灾难性遗忘的缺点，这标志着在面向广泛应用的LLM高效训练方面迈出了重要一步。