Summary of Beyond Iid: Optimizing Instruction Learning From the Perspective Of Instruction Interaction and Dependency, by Hanyu Zhao et al.
Beyond IID: Optimizing Instruction Learning from the Perspective of Instruction Interaction and Dependency
by Hanyu Zhao, Li Du, Yiming Ju, Chengwei Wu, Tengfei Pan
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of selecting and integrating various instructions to fine-tune large language models (LLMs). They investigate how different categories of instructions interact with each other and develop a linear programming-based method to optimize the instruction set. The study also explores how to use this optimized instruction set to improve learning schema using an instruction dependency taxonomy guided curriculum learning approach. Experimental results demonstrate improved performance on widely adopted benchmarks across various LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need fine-tuning with instructions to work well. This paper figures out how different types of instructions affect each other and makes a special method to choose the best instructions. The researchers also show how this optimized instruction set can help improve learning. They test their ideas on big language models and find that it works better than others do. |
Keywords
» Artificial intelligence » Curriculum learning » Fine tuning
