IBM researchers have introduced LAB (Large Scale Alignment for Chatbots) to address scalability challenges encountered during the instruction tuning phase of training large language models (LLM). While LLMs have revolutionized natural language processing (NLP) applications, the instruction tuning phase and fine-tuning of models for specific tasks require high resource requirements and are highly reliable from human annotations and proprietary models such as GPT- 4. This requirement presents challenges in terms of cost, scalability, and access to high-quality training data.
Currently, instruction tuning involves training LLMs on specific tasks using human-annotated data or synthetic data generated by pre-trained models such as GPT-4. These methods are expensive, non-scalable, and may not allow knowledge retention and adaptation to new tasks. To address these challenges, the paper presents LAB (Large Scale Alignment for Chatbots), a novel methodology for instruction tuning. LAB leverages a taxonomy-guided synthetic data generation process and a multi-phase tuning framework to reduce reliance on costly human annotations and proprietary models. This approach aims to improve LLM capabilities and instruction-following behaviors without the drawbacks of catastrophic forgetting, offering a cost-effective and scalable solution for LLM training.
LAB consists of two main components: a taxonomy-based synthetic data generation method and a multi-phase training framework. The taxonomy organizes tasks into branches of knowledge, fundamental skills, and composition skills, allowing for the curation and generation of specific data. Synthetic data generation is guided by taxonomy to ensure the diversity and quality of the data generated. The multi-phase training framework comprises knowledge and skill adjustment phases, with a repetition buffer to avoid catastrophic forgetting. Empirical results demonstrate that models trained on LAB achieve competitive performance on various benchmarks compared to models trained on traditional human-annotated or GPT-4-generated synthetic data. LAB is evaluated using six different metrics, including MT-Bench, MMLU, ARC, HellaSwag, Winograde, and GSM8k, and the results demonstrate that models trained on LAB perform competitively on a wide range of natural language processing tasks, outperforming the excellent previous models. -adjusted by Gpt-4 or human annotated data. LABRADORITE-13B and MERLINITE-7B, aligned with LAB, outperform existing models in chatbot capability while maintaining knowledge and reasoning capabilities.
In conclusion, the article presents LAB as a novel methodology to address scalability challenges in instructional adjustment for LLM. LAB offers a cost-effective and scalable solution to enhance LLM capabilities without catastrophic forgetting by leveraging taxonomy-guided synthetic data generation and a multi-phase training framework. The proposed method achieves state-of-the-art performance in chatbot capability while maintaining knowledge and reasoning capabilities. LAB represents an important step forward in efficient LLM training for a wide range of applications.
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Pragati Jhunjhunwala is a Consulting Intern at MarktechPost. She is currently pursuing B.tech from the Indian Institute of technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in the scope of data science software and applications. She is always reading about the advancements in different fields of ai and ML.
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