Summary of Kif: Knowledge Identification and Fusion For Language Model Continual Learning, by Yujie Feng et al.
KIF: Knowledge Identification and Fusion for Language Model Continual Learning
by Yujie Feng, Xu Chu, Yongxin Xu, Zexin Lu, Bo Liu, Philip S. Yu, Xiao-Ming Wu
First submitted to arxiv on: 9 Aug 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 Knowledge Identification and Fusion (KIF) framework for language models enables continual learning without retraining by leveraging knowledge transfer between tasks. KIF initially segregates the model into ‘skill units’ based on parameter dependencies, allowing for more precise control. It then employs a novel group-wise knowledge identification technique to ascertain the importance distribution of skill units for a new task and updates task-specific knowledge while retaining prior knowledge. This approach achieves an optimal balance between retaining prior knowledge and excelling in new tasks, demonstrating strong generalizability and extensibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning helps language models adapt to changing scenarios without retraining. The proposed KIF framework improves this process by retaining old knowledge and updating new information. It does this by grouping the model’s parameters into ‘skill units’ and finding which ones are most important for a new task. By doing so, it prevents the model from forgetting what it already knows and helps it learn new things better. This approach works well with different-sized models and even integrates with other learning methods. |
Keywords
» Artificial intelligence » Continual learning
