Summary of Core Context Aware Attention For Long Context Language Modeling, by Yaofo Chen et al.
Core Context Aware Attention for Long Context Language Modeling
by Yaofo Chen, Zeng You, Shuhai Zhang, Haokun Li, Yirui Li, Yaowei Wang, Mingkui Tan
First submitted to arxiv on: 17 Dec 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 This paper proposes a novel attention mechanism, Core Context Aware (CCA) Attention, to efficiently model long-range contexts in transformer-based large language models. The self-attention mechanism in LLMs is effective for capturing dependencies between tokens, but as the context length increases, it suffers from computational and memory complexity scaling quadratically with the input length. Additionally, redundant context information can hinder the model’s ability to capture crucial token relationships, degrading representation performance. To address these limitations, CCA Attention consists of globality-pooling attention that focuses on core tokens and locality-preserved attention that incorporates neighboring tokens. The two attentions are fused to maintain comprehensive modeling ability while reducing computational complexity. Experimental results demonstrate that CCA-Attention outperforms state-of-the-art models in terms of efficiency and long-context modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in language models by making them more efficient and better at understanding longer texts. Right now, these models are really good at finding relationships between words, but when they have to look at very long texts, it gets too slow and takes up too much memory. The authors came up with a new way of looking at the text that helps them focus on the most important parts and ignore the extra information that’s not helpful. This makes their models faster and better at understanding longer texts. |
Keywords
» Artificial intelligence » Attention » Context length » Self attention » Token » Transformer
