Summary of Seekr: Selective Attention-guided Knowledge Retention For Continual Learning Of Large Language Models, by Jinghan He et al.
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
by Jinghan He, Haiyun Guo, Kuan Zhu, Zihan Zhao, Ming Tang, Jinqiao Wang
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 SEEKR method for continual learning of large language models (LLMs) utilizes attention distillation on selected attention heads to retain knowledge efficiently. This approach outperforms existing methods in terms of performance and efficiency, achieving comparable or better results with only 1/10th the replayed data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SEEKR is a new method for continual learning that helps language models adapt to changing demands without forgetting previous knowledge. It’s like a superpower for AI models! The approach focuses on the most important parts of the model, identified by special measures called forgettability and task-sensitivity. This makes it more efficient and effective than current methods. |
Keywords
» Artificial intelligence » Attention » Continual learning » Distillation
