Large Language Models for Knowledge-Free Network Management: Feasibility Study and Opportunities
- URL: http://arxiv.org/abs/2410.17259v1
- Date: Sun, 06 Oct 2024 07:42:23 GMT
- Title: Large Language Models for Knowledge-Free Network Management: Feasibility Study and Opportunities
- Authors: Hoon Lee, Mintae Kim, Seunghwan Baek, Namyoon Lee, Merouane Debbah, Inkyu Lee,
- Abstract summary: This article presents a novel knowledge-free network management paradigm with the power of foundation models called large language models (LLMs)
LLMs can understand important contexts from input prompts containing minimal system information, thereby offering remarkable inference performance even for entirely new tasks.
Numerical results validate that knowledge-free LLMs are able to achieve comparable performance to existing knowledge-based optimization algorithms.
- Score: 36.70339455624253
- License:
- Abstract: Traditional network management algorithms have relied on prior knowledge of system models and networking scenarios. In practice, a universal optimization framework is desirable where a sole optimization module can be readily applied to arbitrary network management tasks without any knowledge of the system. To this end, knowledge-free optimization techniques are necessary whose operations are independent of scenario-specific information including objective functions, system parameters, and network setups. The major challenge of this paradigm-shifting approach is the requirement of a hyper-intelligent black-box optimizer that can establish efficient decision-making policies using its internal reasoning capabilities. This article presents a novel knowledge-free network management paradigm with the power of foundation models called large language models (LLMs). Trained on vast amounts of datasets, LLMs can understand important contexts from input prompts containing minimal system information, thereby offering remarkable inference performance even for entirely new tasks. Pretrained LLMs can be potentially leveraged as foundation models for versatile network optimization. By eliminating the dependency on prior knowledge, LLMs can be seamlessly applied for various network management tasks. The viability of this approach is demonstrated for resource management problems using GPT-3.5-Turbo. Numerical results validate that knowledge-free LLM optimizers are able to achieve comparable performance to existing knowledge-based optimization algorithms.
Related papers
- Unified Parameter-Efficient Unlearning for LLMs [25.195126838721492]
Large Language Models (LLMs) have revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks.
This raises significant privacy and security concerns, as models may inadvertently retain and disseminate sensitive or undesirable information.
We introduce a novel instance-wise unlearning framework, LLMEraser, which systematically categorizes unlearning tasks and applies precise adjustments using influence functions.
arXiv Detail & Related papers (2024-11-30T07:21:02Z) - eFedLLM: Efficient LLM Inference Based on Federated Learning [1.6179784294541053]
Large Language Models (LLMs) herald a transformative era in artificial intelligence (AI)
This paper introduces an effective approach that enhances the operational efficiency and affordability of LLM inference.
arXiv Detail & Related papers (2024-11-24T22:50:02Z) - Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving.
Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.
We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
arXiv Detail & Related papers (2024-10-10T17:00:06Z) - EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty.
We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications.
Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
arXiv Detail & Related papers (2024-10-08T17:54:03Z) - Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization [62.16747639440893]
Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering.
Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
arXiv Detail & Related papers (2024-08-07T08:43:32Z) - Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently.
Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting.
We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
arXiv Detail & Related papers (2024-06-14T19:24:00Z) - Can LLMs Understand Computer Networks? Towards a Virtual System Administrator [15.469010487781931]
This paper is the first to conduct an exhaustive study on Large Language Models' comprehension of computer networks.
We evaluate our framework on multiple computer networks employing proprietary (e.g., GPT4) and open-source (e.g., Llama2) models.
arXiv Detail & Related papers (2024-04-19T07:41:54Z) - NetLLM: Adapting Large Language Models for Networking [36.61572542761661]
We present NetLLM, the first framework that provides a coherent design to harness the powerful capabilities of LLMs with low efforts to solve networking problems.
Specifically, NetLLM empowers the LLM to effectively process multimodal data in networking and efficiently generate task-specific answers.
arXiv Detail & Related papers (2024-02-04T04:21:34Z) - FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large
Language Models in Federated Learning [70.38817963253034]
This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution.
We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios.
We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
arXiv Detail & Related papers (2023-09-01T09:40:36Z)
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.