Single Vs Multi-Task LLM Instruction Fine-Tuning

Author(s): Youssef Hosni

Originally published on Towards AI.

The comparative advantages and challenges of single-task versus multi-task fine-tuning of large language models (LLMs) are explored. The discussion begins with single-task fine-tuning, highlighting its benefits and drawbacks, including the issue of catastrophic forgetting.

It then transitions to an overview of multitasking fine-tuning, examining both its challenges and potential benefits. The introduction of FLAN models, specifically the FLAN-T5, demonstrates advancements in multitask instruction tuning.

Detailed guidance on fine-tuning FLAN-T5 for specific applications, such as summarizing customer service chats, illustrates practical use cases. This analysis provides a comprehensive understanding of the strategic considerations involved in choosing between single-task and multitask fine-tuning approaches for LLMs.

Introduction to Single-Task Fine-Tuning1.1. Benefits and Drawbacks of Single-Task Fine-Tuning1.2. Catastrophic Forgetting in Fine-TuningMultitask Fine-Tuning Overview2.1. Challenges and Benefits of Multitask Fine-Tuning2.2. Introduction to FLAN Models2.3. Overview of FLAN-T52.4. Fine-Tuning FLAN-T5 for Specific Use CasesExample: Summarizing Customer Service Chats

Most insights I share in Medium have previously been shared in my weekly newsletter, To Data & Beyond.

If you want to be up-to-date with the frenetic world of AI while also feeling inspired to take action or, at the very least, to be well-prepared for the future ahead of us, this is for you.

🏝Subscribe below🏝 to become an AI leader among your… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI