Summary of Quickllama: Query-aware Inference Acceleration For Large Language Models, by Jingyao Li et al.
QuickLLaMA: Query-aware Inference Acceleration for Large Language Models
by Jingyao Li, Han Shi, Xin Jiang, Zhenguo Li, Hong Xu, Jiaya Jia
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Query-aware Inference for Large Language Models (Q-LLM) aims to improve the ability of LLMs to comprehend and reason over long contexts by focusing on memory data relevant to a given query. This system can accurately capture pertinent information within a fixed window size and provide precise answers to queries, without requiring extra training. Q-LLM has been integrated with LLaMA3 (QuickLLaMA) and demonstrated improved performance on various benchmarks, including a 7.17% increase over the current state-of-the-art on LLaMA3 and a 3.26% improvement on Mistral. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have the ability to understand and reason about long sequences of text. However, they struggle with capturing long-distance dependencies within these sequences. To address this issue, researchers have developed Query-aware Inference for Large Language Models (Q-LLM). This system allows LLMs to focus on specific pieces of information that are relevant to a given query, rather than trying to understand the entire sequence at once. Q-LLM is designed to be easy to use and can be integrated with any existing LLM. |
Keywords
» Artificial intelligence » Inference
