Summary of Phased Instruction Fine-tuning For Large Language Models, by Wei Pang and Chuan Zhou and Xiao-hua Zhou and Xiaojie Wang
Phased Instruction Fine-Tuning for Large Language Models
by Wei Pang, Chuan Zhou, Xiao-Hua Zhou, Xiaojie Wang
First submitted to arxiv on: 1 Jun 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 The proposed Phased Instruction Fine-Tuning (Phased IFT) method enhances pre-trained language models’ ability to follow complex instructions by gradually learning to adhere to increasingly difficult tasks. By dividing instruction data into subsets based on difficulty and uptraining the model sequentially, Phased IFT outperforms existing One-off Instruction Fine-Tuning methods. Experiments with various large language models and datasets demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Phased Instruction Fine-Tuning is a new way to improve big language models so they can better follow instructions. Right now, these models are only good at guessing the next word in a sentence. But Phased IFT helps them understand more complex instructions by gradually teaching them to follow increasingly hard tasks. The results show that this method works well and is a simple way to make large language models smarter. |
Keywords
» Artificial intelligence » Fine tuning
