Summary of Ltri-llm: Streaming Long Context Inference For Llms with Training-free Dynamic Triangular Attention Pattern, by Hongyin Tang et al.
Ltri-LLM: Streaming Long Context Inference for LLMs with Training-Free Dynamic Triangular Attention Pattern
by Hongyin Tang, Di Xiu, Lanrui Wang, Xiurui Geng, Jingang Wang, Xunliang Cai
First submitted to arxiv on: 6 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 The proposed Ltri-LLM framework addresses the quadratic computational complexity of attention mechanisms in Large Language Models (LLMs) by dividing Key-Value pairs into spans and storing them in an offline index. This enables efficient, streaming-based inference for virtually unlimited text lengths while achieving performance close to Full Attention (FA). The framework leverages local correlations in attention head patterns, reflecting a chunking mechanism for input context. Experimental results on long text benchmarks demonstrate the efficacy of Ltri-LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem with large language models that makes it hard to understand very long texts. Right now, these models use an “attention” mechanism that helps them focus on important parts of the text. However, this mechanism becomes too slow when dealing with really long texts. To fix this, the researchers developed a new framework called Ltri-LLM. This framework groups information into smaller chunks and stores it in a special index. This makes it much faster to understand very long texts while still being able to get accurate results. |
Keywords
» Artificial intelligence » Attention » Inference
