Reproduce “Beyond IID: Optimizing Instruction Learning from the Perspective of Instruction Interaction and Dependency” on OpenResearch

About this paper

Hanyu Zhao, Li Du, Yiming Ju, Chengwei Wu, Tengfei Pan

Investigates instruction interaction and dependency in supervised fine-tuning (SFT) for large language models (LLMs), revealing synergistic and antagonistic relationships. The study introduces methods to optimize instruction set composition and learning order based on these findings, demonstrating performance improvements over IID-based approaches.

Key insights

• Instruction categories in supervised fine-tuning (SFT) datasets exhibit widespread synergistic and antagonistic interaction patterns, directly challenging the traditional independent and identically distributed (IID) assumption.
• Large language models possess an empirical hierarchical ability taxonomy, where foundational skills like math and coding are prerequisites for more complex tasks.
• Optimizing instruction set composition and learning order based on these identified interactions and dependencies consistently improves LLM performance and training efficiency.

Summary by alphaXiv.