Summary of Strategic Data Ordering: Enhancing Large Language Model Performance Through Curriculum Learning, by Jisu Kim et al.
Strategic Data Ordering: Enhancing Large Language Model Performance through Curriculum Learning
by Jisu Kim, Juhwan Lee
First submitted to arxiv on: 13 May 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 This study proposes a curriculum learning-inspired strategy for training Large Language Models (LLMs), starting with simpler tasks and progressing to more complex ones. The approach uses criteria like prompt length, attention scores, and loss values to structure the training data. Experiments with Mistral-7B and Gemma-7B models show that this method slightly improves performance compared to traditional random data shuffling. Notably, sorting data based on proposed attention criteria generally leads to better performance. This approach offers a sustainable way to enhance LLM performance without increasing model size or dataset volume, addressing scalability challenges in LLM training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores new ways to train special kinds of AI models called Large Language Models (LLMs). These models are good at understanding and generating text. The authors propose a new method that starts with simple tasks and gets harder as it goes along. This helps the model learn better without needing more computer power or data. They tested this approach on two different models, Mistral-7B and Gemma-7B, and found that it worked slightly better than usual. The goal is to make LLMs more efficient and easier to use. |
Keywords
» Artificial intelligence » Attention » Curriculum learning » Prompt
