LAB: Large-Scale Alignment for ChatBots (2403.01081v3)

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 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.

• The paper presents LAB's main contribution: a taxonomy-guided synthetic data generation process that minimizes dependency on extensive human annotations.
• The methodology employs a two-phase training framework, first focusing on knowledge tuning then on skills, validated through benchmarks like MMLU and MT-Bench.
• The approach demonstrates practical success by producing LAB-aligned models, such as Labradorite-13b and Malachite-7B, competitive with established training methods.

Unveiling LAB: A New Horizon in Large-Scale Alignment for Chatbots

Efficient Synthetic Data Generation for LLMs

LLMs have marked their presence significantly in the field of NLP, challenged constantly by the need for efficient and scalable instruction-tuning strategies. The paper introduces LAB (Large-scale Alignment for chatBots), a novel approach aiming to refine this segment of LLM training. LAB emphasizes a methodology that combines a synthetic data generation process steered by a unique taxonomy and a multi-phase tuning framework. This integrated strategy significantly diminishes the dependency on extensive human annotations and proprietary models such as GPT-4, marking a step forward in LLM training efficiency.

Taxonomy-Guided Data Generation and Quality Assurance

At its core, LAB incorporates a taxonomy that categorizes data into finer task groups, facilitating ease of identification for missing tasks or areas of interest. This taxonomy diverges into knowledge, foundational skills, and compositional skills, systematically covering the landscape of required data for LLM instruction tuning. LAB’s strength lies in its two Synthetic Data Generators (SDGs) - one focusing on skills generation and the other on knowledge generation, both ensuring high diversity and quality in the generated datapoints.

Phased Training Framework

LAB’s training protocol unfolds in two primary phases - knowledge tuning, followed by skills tuning, with a unique incorporation of a replay buffer to counter catastrophic forgetting. The phased approach starts with training the model on knowledge and foundational skills before progressing to compositional skills. The methodology optimizes model performance by utilizing benchmarks like MMLU and MT-bench for intermediate evaluation, ensuring the model's alignment with a broad scope of tasks.

Benchmarking and Results

Implemented on open models like \textsc{Llama-2-13b} and \textsc{Mistral-7B}, LAB leverages \textsc{Mixtral-8x7B-Instruct} as the teacher model. This setup produced two LAB-aligned models: \textsc{Labradorite-13b} and \textsc{Malachite-7B}, competitive against contemporary models trained with traditionally expensive or enclosed methods. The LAB models exhibited commendable performance across various benchmarks, notably achieving state-of-the-art performance on MT-Bench among models fine-tuned on \textsc{Llama-2-13b} and \textsc{Mistral-7B} bases. This showcases LAB's potential to maintain superior chatbot capabilities and knowledge or reasoning capability, leveraging a less costly and openly available teacher model.

The Implications and The Road Ahead

LAB's methodology presents a promising avenue for scaling the instruction-tuning phase of LLMs more efficiently and cost-effectively. By reducing reliance on expensive human annotations and proprietary models for synthetic data generation, LAB opens new possibilities for enhancing LLMs' capabilities and instruction-following behaviors. Its success posits the utility of taxonomy-guided synthetic data generation and multi-phase training frameworks as vital components in the future landscape of AI and machine learning.

The implications of such a framework extend beyond present achievements, hinting at a future where LLM training can be democratized, and innovations in AI can be made more accessible. As LLMs continue to evolve and find new applications, methodologies like LAB serve as critical milestones in the journey towards more sophisticated, efficient, and inclusive AI development processes.