LAB: Large-Scale Alignment for ChatBots
<|reference_start|>LAB: Large-Scale Alignment for ChatBots: 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.<|reference_end|>
@article{sudalairaj2024lab:,
    title={LAB: Large-Scale Alignment for ChatBots},
    author={Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu,
  David D. Cox, Akash Srivastava},
    journal={arXiv preprint arXiv:2403.01081},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01081},
    primaryClass={cs.CL cs.LG}
}