Summary of Pace: a Pragmatic Agent For Enhancing Communication Efficiency Using Large Language Models, by Jiaxuan Li and Minxi Yang and Dahua Gao and Wenlong Xu and Guangming Shi
PACE: A Pragmatic Agent for Enhancing Communication Efficiency Using Large Language Models
by Jiaxuan Li, Minxi Yang, Dahua Gao, Wenlong Xu, Guangming Shi
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 framework for pragmatic communication leverages Large Language Models (LLMs) to optimize data transmission, addressing limitations in current communication technologies. The Pragmatic Agent for Communication Efficiency (PACE) uses LLMs to sequentially perceive semantics, resolve intentions, and code messages. A knowledge base supplements necessary information, dedicated prompts facilitate scenario understanding, and a chain of thought assists in balancing efficiency and cost. Experimental results demonstrate improved transmission efficiency compared to traditional and non-LLM-based approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pragmatic communication is a way for devices to send data more efficiently. Right now, our current technology has some limitations when it comes to how much data can be sent, how much spectrum is available, and how much power is used. A new approach called PACE (Pragmatic Agent for Communication Efficiency) uses special language models to make communication more efficient. This involves understanding what the message means, figuring out what the sender wants to say, and then encoding the message in a way that uses the least amount of resources needed. To help this process work well, a database with important information is created, special prompts are designed to help understand scenarios, and a thinking path is made to balance efficiency and cost. The results show that PACE outperforms other methods in sending data efficiently. |
Keywords
» Artificial intelligence » Knowledge base » Semantics
