Summary of Livemind: Low-latency Large Language Models with Simultaneous Inference, by Chuangtao Chen and Grace Li Zhang and Xunzhao Yin and Cheng Zhuo and Ulf Schlichtmann and Bing Li
LiveMind: Low-latency Large Language Models with Simultaneous Inference
by Chuangtao Chen, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann, Bing Li
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 introduces LiveMind, a novel low-latency inference framework for large language models (LLMs) that enables them to perform inferences with incomplete user input. The framework reallocates computational processes to the input phase, reducing latency by 84% on average compared to traditional methods. It also facilitates collaborative inference and output across different models, improving accuracy by 4.3%. This advancement enhances human-AI interaction, enabling more responsive and efficient communication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LiveMind is a new way for large language models (AI systems) to understand what people are saying even if they don’t finish their thoughts. It makes AI conversations feel more natural and fast-paced. The paper shows that this approach can speed up responses by 84% while still being very accurate. This could make it easier for humans and AI to work together. |
Keywords
» Artificial intelligence » Inference
