Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs)

• URL: http://arxiv.org/abs/2403.17819v1
• Date: Tue, 26 Mar 2024 15:54:48 GMT
• Title: Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs)
• Authors: Amir Ghasemi, Paul Guinand,
• Abstract summary: This paper demonstrates example applications of Large Language Models (LLMs) to expedite spectrum regulatory processes. We explore various roles that LLMs can play in this context while identifying some of the challenges to address.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wireless spectrum regulation is a complex and demanding process due to the rapid pace of technological progress, increasing demand for spectrum, and a multitude of stakeholders with potentially conflicting interests, alongside significant economic implications. To navigate this, regulators must engage effectively with all parties, keep pace with global technology trends, conduct technical evaluations, issue licenses in a timely manner, and comply with various legal and policy frameworks. In light of these challenges, this paper demonstrates example applications of Large Language Models (LLMs) to expedite spectrum regulatory processes. We explore various roles that LLMs can play in this context while identifying some of the challenges to address. The paper also offers practical case studies and insights, with appropriate experiments, highlighting the transformative potential of LLMs in spectrum management.

Related papers

• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• The Impossibility of Fair LLMs [59.424918263776284]
  The need for fair AI is increasingly clear in the era of large language models (LLMs) We review the technical frameworks that machine learning researchers have used to evaluate fairness. We develop guidelines for the more realistic goal of achieving fairness in particular use cases.
  arXiv  Detail & Related papers  (2024-05-28T04:36:15Z)
• Practices, Challenges, and Opportunities When Inferring Requirements From Regulations in the FinTech Sector - An Industrial Study [1.0936851319953484]
  Understanding and interpreting regulatory norms and inferring software requirements from them is a critical step towards regulatory compliance. This study investigates the complexities of requirement engineering in regulatory contexts, pinpointing various issues and discussing them in detail. We have identified key practices for managing regulatory requirements in software development, and have pinpointed several challenges.
  arXiv  Detail & Related papers  (2024-05-05T09:39:08Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey [1.0770079992809338]
  The capabilities of Large Language Models (LLMs) are increasingly demonstrating unique roles in the legal sector. This survey delves into the synergy between LLMs and the legal system, such as their applications in tasks like legal text comprehension, case retrieval, and analysis. The survey showcases the latest advancements in fine-tuned legal LLMs tailored for various legal systems, along with legal datasets available for fine-tuning LLMs in various languages.
  arXiv  Detail & Related papers  (2024-04-01T08:35:56Z)
• Large Multimodal Agents: A Survey [78.81459893884737]
  Large language models (LLMs) have achieved superior performance in powering text-based AI agents. There is an emerging research trend focused on extending these LLM-powered AI agents into the multimodal domain. This review aims to provide valuable insights and guidelines for future research in this rapidly evolving field.
  arXiv  Detail & Related papers  (2024-02-23T06:04:23Z)
• Large Language Models for Telecom: Forthcoming Impact on the Industry [13.456882619578707]
  Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force. We delve into the inner workings of LLMs, providing insights into their current capabilities and limitations. We uncover essential research directions that deal with the distinctive challenges of utilizing the LLMs within the telecom domain.
  arXiv  Detail & Related papers  (2023-08-11T08:41:00Z)
• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [100.24095818099522]
  Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) They provide a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles.
  arXiv  Detail & Related papers  (2023-05-30T03:00:30Z)
• Auditing large language models: a three-layered approach [0.0]
  Large language models (LLMs) represent a major advance in artificial intelligence (AI) research. LLMs are also coupled with significant ethical and social challenges. Previous research has pointed towards auditing as a promising governance mechanism.
  arXiv  Detail & Related papers  (2023-02-16T18:55:21Z)

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.