Large Language Models (LLMs) have made advancements in text understanding and generation. However, they face challenges in effective human instruction delivery. To tackle this, Microsoft’s research introduces GLAN, a scalable approach inspired by the human education system. GLAN provides comprehensive, diverse, and task-agnostic instructions, offering flexibility and the ability to easily expand dataset domains and skills.
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Introducing GLAN: A Practical AI Solution for Instruction Tuning of Large Language Models (LLMs)
Challenges in Optimizing Large Language Models
Large Language Models (LLMs) have made significant advancements in text understanding and generation. However, optimizing LLMs for effective human instruction delivery has been challenging.
Instruction Tuning as a Solution
Instruction tuning involves fine-tuning LLMs on instructions matched with replies that humans like. Existing techniques often rely on scarce NLP datasets or self-instruct approaches, leading to limited diversity. Evolve-Instruct uses data augmentation to enhance datasets, but this still has limitations.
Introducing GLAN
Microsoft’s GLAN (Generalized Instruction Tuning) is influenced by the organized framework of the human education system. It breaks down human knowledge into domains, sub-fields, and disciplines using LLMs and human verification. GLAN is flexible, scalable, and task-agnostic, producing instructions on a large scale across various disciplines.
Benefits of GLAN
GLAN is scalable, customizable, and efficient, producing a wide range of instructions without task-specific training data. It simplifies customization and allows for easy expansion and changes without starting from scratch.
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