Summary of Towards Infinite-long Prefix in Transformer, by Yingyu Liang et al.
Towards Infinite-Long Prefix in Transformer
by Yingyu Liang, Zhenmei Shi, Zhao Song, Chiwun Yang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper proposes a new approach to fine-tuning language models called Prefix Learning, which aims to enhance their performance on various downstream tasks. The authors investigate the effectiveness of prompting and context-based methods in a stylized setting using the Neural Tangent Kernel (NTK) framework. They provide a convergence guarantee for training an ultra-long prefix and design an algorithm that only requires a few extra trainable parameters. Experimental results show that this method achieves superior or competitive performance compared to existing approaches like full parameter fine-tuning, P-Tuning V2, and LoRA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prefix Learning is a new way to make language models better at understanding specific tasks. The authors wanted to see if their approach could work well without needing to change the whole model. They tested it on vision, natural language, and math data and found that it did really well compared to other methods! |
Keywords
» Artificial intelligence » Fine tuning » Lora » Prompting
