Summary of Boosting Long-context Management Via Query-guided Activation Refilling, by Hongjin Qian et al.
Boosting Long-Context Management via Query-Guided Activation Refilling
by Hongjin Qian, Zheng Liu, Peitian Zhang, Zhicheng Dou, Defu Lian
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 addresses the limitations of large language models (LLMs) in processing long contexts by introducing an efficient approach that adapts to dynamic information needs. Specifically, it tackles the issues of context-window limitations and computational burden associated with extensive key-value (KV) activations, which hinder efficiency. The authors recognize that for information-seeking tasks, full context perception is not always necessary, as queries’ information needs can range from localized details to a global perspective depending on complexity. They propose a method that effectively adapts to these dynamic information needs, enabling LLMs to better serve their purpose. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) struggle to process long texts because they’re limited in how much context they can understand at once. This makes them slow and uses too many computer resources. The authors of this paper want to help by making the model more efficient. They know that when we search for information, we often don’t need everything about a topic – just some specific details or a general overview. So, they’re working on a way to make the model adapt to what we need, so it can give us better answers. |
Keywords
» Artificial intelligence » Context window
