Integrating Large Language Models with Internet of Things Applications

• URL: http://arxiv.org/abs/2410.19223v1
• Date: Fri, 25 Oct 2024 00:21:45 GMT
• Title: Integrating Large Language Models with Internet of Things Applications
• Authors: Mingyu Zong, Arvin Hekmati, Michael Guastalla, Yiyi Li, Bhaskar Krishnamachari,
• Abstract summary: This paper identifies and analyzes applications in which Large Language Models (LLMs) can make Internet of Things (IoT) networks more intelligent and responsive. Our results reveal that the GPT model under few-shot learning achieves 87.6% detection accuracy, whereas the fine-tuned GPT increases the value to 94.9%.
• Score: 6.22153888560487
• License:
• Abstract: This paper identifies and analyzes applications in which Large Language Models (LLMs) can make Internet of Things (IoT) networks more intelligent and responsive through three case studies from critical topics: DDoS attack detection, macroprogramming over IoT systems, and sensor data processing. Our results reveal that the GPT model under few-shot learning achieves 87.6% detection accuracy, whereas the fine-tuned GPT increases the value to 94.9%. Given a macroprogramming framework, the GPT model is capable of writing scripts using high-level functions from the framework to handle possible incidents. Moreover, the GPT model shows efficacy in processing a vast amount of sensor data by offering fast and high-quality responses, which comprise expected results and summarized insights. Overall, the model demonstrates its potential to power a natural language interface. We hope that researchers will find these case studies inspiring to develop further.

Related papers

• Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models [0.0]
  This paper introduces a novel approach to enhancing closed-domain Question Answering (QA) systems. It focuses on the specific needs of the Lawrence Berkeley National Laboratory (LBL) Science Information Technology (ScienceIT) domain.
  arXiv  Detail & Related papers  (2024-10-24T00:49:46Z)
• Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation [2.9921619703037274]
  We propose a retrieval augmented generation (RAG) framework backed by a large language model (LLM) to correct the output of a smaller model for the linguistic task of morphological glossing. We leverage linguistic information to make up for the lack of data and trainable parameters, while allowing for inputs from written descriptive grammars interpreted and distilled through an LLM. We show that a compact, RAG-supported model is highly effective in data-scarce settings, achieving a new state-of-the-art for this task and our target languages.
  arXiv  Detail & Related papers  (2024-10-01T04:20:14Z)
• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• Automated Text Scoring in the Age of Generative AI for the GPU-poor [49.1574468325115]
  We analyze the performance and efficiency of open-source, small-scale generative language models for automated text scoring. Results show that GLMs can be fine-tuned to achieve adequate, though not state-of-the-art, performance.
  arXiv  Detail & Related papers  (2024-07-02T01:17:01Z)
• ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? [2.3555053092246125]
  We present preliminary experimental results on the ChatGPT research preview, showing that ChatGPT achieves state-of-the-art performance in zero-shot DST. We theorize that the in-context learning capabilities of such models will likely become powerful tools to support the development of dedicated and dynamic dialogue state trackers.
  arXiv  Detail & Related papers  (2023-06-02T09:15:01Z)
• Sensitivity and Robustness of Large Language Models to Prompt Template in Japanese Text Classification Tasks [0.0]
  A critical issue has been identified within this domain: the inadequate sensitivity and robustness of large language models towards Prompt templates. This paper explores this issue through a comprehensive evaluation of several representative Large Language Models (LLMs) and a widely-utilized pre-trained model(PLM) Our experimental results reveal startling discrepancies. A simple modification in the sentence structure of the Prompt template led to a drastic drop in the accuracy of GPT-4 from 49.21 to 25.44.
  arXiv  Detail & Related papers  (2023-05-15T15:19:08Z)
• AutoML-GPT: Automatic Machine Learning with GPT [74.30699827690596]
  We propose developing task-oriented prompts and automatically utilizing large language models (LLMs) to automate the training pipeline. We present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyper parameters. This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas.
  arXiv  Detail & Related papers  (2023-05-04T02:09:43Z)
• Emergent autonomous scientific research capabilities of large language models [0.0]
  Transformer-based large language models are rapidly advancing in the field of machine learning research. We present an Intelligent Agent system that combines multiple large language models for autonomous design, planning, and execution of scientific experiments.
  arXiv  Detail & Related papers  (2023-04-11T16:50:17Z)
• Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning [104.58874584354787]
  In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
  arXiv  Detail & Related papers  (2023-01-27T18:59:01Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.