LAB: Large-scale Alignment For Chatbots

Sudalairaj Shivchander, Bhandwaldar Abhishek, Pareja Aldo, Xu Kai, Cox David D., Srivastava Akash. Arxiv 2024

[Paper]    
Applications Fine Tuning GPT Model Architecture RAG Tools Training Techniques

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.

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