Learning to Remember, Learn, and Forget in Attention-Based Models
📰 ArXiv cs.AI
Learn how to improve attention-based models by addressing the stability-plasticity dilemma in in-context learning
Action Steps
- Implement Palimpsa, a self-attention model that views in-context learning as a continual learning problem
- Address the stability-plasticity dilemma in gated linear attention models
- Experiment with different attention mechanisms to reduce interference in long sequences
- Evaluate the performance of Palimpsa on benchmark tasks
- Compare the results with existing transformer-based models
Who Needs to Know This
Researchers and engineers working on transformer-based models can benefit from this knowledge to improve their models' performance on complex sequence processing tasks
Key Insight
💡 In-context learning in transformers can be improved by addressing the stability-plasticity dilemma, allowing for more efficient and effective processing of complex sequences
Share This
🤖 Improve attention-based models with Palimpsa, a self-attention model that tackles the stability-plasticity dilemma in in-context learning
Key Takeaways
Learn how to improve attention-based models by addressing the stability-plasticity dilemma in in-context learning
Full Article
Title: Learning to Remember, Learn, and Forget in Attention-Based Models
Abstract:
arXiv:2602.09075v3 Announce Type: replace-cross Abstract: In-Context Learning (ICL) in transformers acts as an online associative memory and is believed to underpin their high performance on complex sequence processing tasks. However, in gated linear attention models, this memory has a fixed capacity and is prone to interference, especially for long sequences. We propose Palimpsa, a self-attention model that views ICL as a continual learning problem that must address a stability-plasticity dilem
Abstract:
arXiv:2602.09075v3 Announce Type: replace-cross Abstract: In-Context Learning (ICL) in transformers acts as an online associative memory and is believed to underpin their high performance on complex sequence processing tasks. However, in gated linear attention models, this memory has a fixed capacity and is prone to interference, especially for long sequences. We propose Palimpsa, a self-attention model that views ICL as a continual learning problem that must address a stability-plasticity dilem
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