LAB: Large-Scale Alignment for ChatBots

TL;DR

LAB introduces a scalable, cost-effective method for training large language models using synthetic data generation and multi-phase tuning, reducing dependence on costly human annotations and proprietary models.

Contribution

The paper presents LAB, a novel scalable framework for instruction tuning of LLMs that leverages taxonomy-guided synthetic data and multi-phase training to improve efficiency and performance.

Findings

LAB-trained models perform competitively on benchmarks.

Reduces reliance on expensive human annotations.

Achieves scalable instruction-following capabilities.

Abstract

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.