  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.
