Lecture image placeholder

Premium content

Access to this content requires a subscription. You must be a premium user to view this content.

Monthly subscription - $9.99Pay per view - $4.99Access through your institutionLogin with Underline account
Need help?
Contact us
Lecture placeholder background

poster

ACL 2024

August 22, 2024

Bangkok, Thailand

Demystifying Instruction Mixing for Fine-tuning Large Language Models

keywords:

instruction mixture

instruction tuning

llm

Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datasets for LLM fine-tuning is still poorly understood. This study categorizes instructions into three primary types: NLP downstream tasks, coding, and general chat. We explore the effects of instruction tuning on different combinations of datasets on LLM performance, and find that certain instruction types are more advantageous for specific applications but can negatively impact other areas. This work provides insights into instruction mixtures, laying the foundations for future research.

Next from ACL 2024

CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation
poster

CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation

ACL 2024

+9Pei Ke
Pei Ke and 11 other authors

22 August 2024

Stay up to date with the latest Underline news!

Select topic of interest (you can select more than one)

PRESENTATIONS

  • All Lectures
  • For Librarians
  • Resource Center
  • Free Trial
Underline Science, Inc.
1216 Broadway, 2nd Floor, New York, NY 10001, USA

© 2023 Underline - All rights reserved