Summary of Efficient Prompting For Llm-based Generative Internet Of Things, by Bin Xiao et al.
Efficient Prompting for LLM-based Generative Internet of Things
by Bin Xiao, Burak Kantarci, Jiawen Kang, Dusit Niyato, Mohsen Guizani
First submitted to arxiv on: 14 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 The paper proposes a novel Large Language Model (LLM)-based Generative Internet of Things (GIoT) system that utilizes open-source LLMs in a local network setting. To overcome the limitations of open-source LLMs, the authors apply prompt engineering methods to enhance their capacities and design a Prompt Management Module and Post-processing Module to manage tailored prompts for different tasks. As a case study, the paper presents a challenging Table Question Answering (Table-QA) task, demonstrating competitive performance on two popular datasets compared to state-of-the-art LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed system is designed to make large language models more accessible and usable in Internet of Things applications. By using open-source models and improving their performance with prompt engineering, the system can provide similar results to commercial models without needing to access them online. This could be important for organizations that have security concerns about accessing state-of-the-art LLM services. |
Keywords
» Artificial intelligence » Large language model » Prompt » Question answering
