Summary of Efficient Length-generalizable Attention Via Causal Retrieval For Long-context Language Modeling, by Xiang Hu et al.
Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling
by Xiang Hu, Zhihao Teng, Jun Zhao, Wei Wu, Kewei Tu
First submitted to arxiv on: 2 Oct 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 paper presents a novel attention mechanism called Grouped Cross Attention (GCA), designed to handle long contexts while maintaining computational and memory efficiency. The current state-of-the-art Transformers struggle with generalizing long contexts due to their limited length generalization and quadratic complexity of self-attention, requiring post-training with larger attention windows. GCA addresses this issue by dynamically splitting input sequences into chunks, retrieving top-k relevant past chunks for subsequent text generation, and minimizing the auto-regressive loss of subsequent tokens in an end-to-end manner. This approach allows for long-range information access while reducing computational and memory costs during training and inference. The proposed method is evaluated on passkey retrieval tasks, achieving near-perfect accuracy with 16M context lengths, which is 1000 times the training length. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new attention mechanism that can handle very long texts without needing extra computer power or memory. This is important because current AI models struggle to understand and generate text longer than a few hundred words. The authors call this new approach “Grouped Cross Attention” (GCA) and show that it works well for tasks like passkey retrieval. |
Keywords
» Artificial intelligence » Attention » Cross attention » Generalization » Inference » Self attention » Text generation
