LAB: Large-Scale Alignment for ChatBots

arXiv:2403.01081v1 Announce Type: cross
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 …

abstract alignment annotations arxiv challenges chatbots cs.cl cs.lg data framework human lab language language model large language large language model llm methodology novel process proprietary proprietary models reliance scalability scale synthetic synthetic data taxonomy training type work