Microsoft AI Release Instruct Pre-Training: Enhancing Language Model Pre-Training with Supervised Multitask Learning
How does InstructPT enhance model generalization capabilities?
InstructPT enhances model generalization capabilities by incorporating instruction-response pairs generated from raw text, which enables the model to learn from a variety of tasks embedded within the raw text. This supervised multitask learning framework allows pre-trained models to improve their base performance and significantly benefits from further instruction tuning, resulting in better performance on diverse and unseen tasks.
What are the different variants of the InstructPT framework?
The InstructPT framework has several variants, including instruction-pretrain/medicine-Llama3-8B for biomedical applications, instruction-pretrain/InstructLM-1.3B for general-purpose use, instruction-pretrain/instruction-synthesizer for generating instruction-response pairs, instruction-pretrain/InstructLM-500M for efficiency, and instruction-pretrain/finance-Llama3-8B for financial tasks. These variants are tailored to specific domains and tasks, enhancing performance in their respective fields.
How does InstructPT differ from Vanilla Pre-Training?
InstructPT and Vanilla Pre-Training are both methods for pre-training language models. InstructPT leverages supervised multitask learning and incorporates instruction-response pairs generated from raw text, enhancing the model's generalization ability2. Vanilla Pre-Training, on the other hand, relies on unsupervised learning from raw corpora2. InstructPT has shown to improve base performance and resource efficiency compared to Vanilla Pre-Training.