Summary of Infinipot: Infinite Context Processing on Memory-constrained Llms, by Minsoo Kim et al.
InfiniPot: Infinite Context Processing on Memory-Constrained LLMs
by Minsoo Kim, Kyuhong Shim, Jungwook Choi, Simyung Chang
First submitted to arxiv on: 2 Oct 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 A novel framework called InfiniPot is introduced to enable Large Language Models (LLMs) to efficiently manage long input contexts within fixed memory constraints, without requiring additional training. The framework leverages Continual Context Distillation (CCD), an iterative process that compresses and retains essential information through importance metrics. This allows LLMs to maintain critical data even without access to future context. InfiniPot is shown to significantly outperform models trained for long contexts in various NLP tasks, establishing its efficacy and versatility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InfiniPot is a new way to make Large Language Models (LLMs) work better with long pieces of text. Right now, it’s hard for LLMs to handle really long texts on devices like phones because they need too much memory. InfiniPot helps by keeping the most important parts of the text and discarding the rest. This makes it possible to use LLMs in more real-world situations. |
Keywords
» Artificial intelligence » Distillation » Nlp
