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LAB: Large-Scale Alignment for ChatBots

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arxiv 2403.01081 v3 pith:O6SVH3KQ submitted 2024-03-02 cs.CL cs.LG

Shivchander Sudalairaj , Abhishek Bhandwaldar , Aldo Pareja , Kai Xu , David D. Cox , Akash Srivastava This is my paper · ORCID
classification cs.CLcs.LG
keywords modelsalignmentchatbotsdatagpt-4large-scalesynthetictraining
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 6 Pith papers

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