Summary of In-context Continual Learning Assisted by An External Continual Learner, By Saleh Momeni et al.
In-context Continual Learning Assisted by an External Continual Learner
by Saleh Momeni, Sahisnu Mazumder, Zixuan Ke, Bing Liu
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 approach, InCA, integrates an external continual learner (ECL) with in-context learning (ICL) to enable scalable continual learning without catastrophic forgetting. It addresses the limitations of ICL by incrementally building the ECL to pre-select likely classes for each test instance, restricting the prompt length and maintaining performance. The method is evaluated against existing CL baselines, showing significant performance gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InCA is a new way to learn from data without forgetting what we already know. It combines two techniques: in-context learning and an external continual learner. This helps us learn more efficiently and avoid losing knowledge as time goes on. The approach shows promising results and could have important applications in areas like language processing. |
Keywords
» Artificial intelligence » Continual learning » Prompt
