Large Language Models for Telecom: Forthcoming Impact on the Industry
- URL: http://arxiv.org/abs/2308.06013v2
- Date: Sun, 25 Feb 2024 23:06:28 GMT
- Title: Large Language Models for Telecom: Forthcoming Impact on the Industry
- Authors: Ali Maatouk, Nicola Piovesan, Fadhel Ayed, Antonio De Domenico,
Merouane Debbah
- Abstract summary: 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.
- Score: 13.456882619578707
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs), AI-driven models that can achieve
general-purpose language understanding and generation, have emerged as a
transformative force, revolutionizing fields well beyond Natural Language
Processing (NLP) and garnering unprecedented attention. As LLM technology
continues to progress, the telecom industry is facing the prospect of its
impact on its landscape. To elucidate these implications, we delve into the
inner workings of LLMs, providing insights into their current capabilities and
limitations. We also examine the use cases that can be readily implemented in
the telecom industry, streamlining tasks, such as anomalies resolutions and
technical specifications comprehension, which currently hinder operational
efficiency and demand significant manpower and expertise. Furthermore, we
uncover essential research directions that deal with the distinctive challenges
of utilizing the LLMs within the telecom domain. Addressing them represents a
significant stride towards fully harnessing the potential of LLMs and unlocking
their capabilities to the fullest extent within the telecom domain.
Related papers
- A Reality check of the benefits of LLM in business [1.9181612035055007]
Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks.
This paper thoroughly examines the usefulness and readiness of LLMs for business processes.
arXiv Detail & Related papers (2024-06-09T02:36:00Z) - Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities [36.711166825551715]
Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities.
This work aims to provide a comprehensive overview of LLM-enabled telecom networks.
arXiv Detail & Related papers (2024-05-17T14:46:13Z) - Unmemorization in Large Language Models via Self-Distillation and
Deliberate Imagination [58.36408867180233]
Large Language Models (LLMs) struggle with crucial issues of privacy violation and unwanted exposure of sensitive data.
We introduce a novel approach termed deliberate imagination in the context of LLM unlearning.
Our results demonstrate the usefulness of this approach across different models and sizes, and also with parameter-efficient fine-tuning.
arXiv Detail & Related papers (2024-02-15T16:21:14Z) - Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
We explore machine unlearning in the domain of large language models (LLMs)
This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
arXiv Detail & Related papers (2024-02-13T20:51:58Z) - Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks.
The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human.
These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
arXiv Detail & Related papers (2024-01-30T17:38:54Z) - Penetrative AI: Making LLMs Comprehend the Physical World [3.0266193917041306]
Large Language Models (LLMs) have demonstrated remarkable capabilities across a range of tasks.
This paper explores how LLMs can be extended to interact with and reason about the physical world through IoT sensors and actuators.
arXiv Detail & Related papers (2023-10-14T15:48:15Z) - Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue.
By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights.
This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
arXiv Detail & Related papers (2023-10-10T03:06:38Z) - 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) - On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation.
Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
arXiv Detail & Related papers (2023-05-23T04:10:26Z) - Large Language Models Humanize Technology [6.127963013089406]
Large Language Models (LLMs) have made rapid progress in recent months and weeks.
This has sparked concerns about aligning these models with human values, their impact on labor markets, and the potential need for regulation.
We argue that LLMs exhibit emergent abilities to humanize technology more effectively than previous technologies.
arXiv Detail & Related papers (2023-05-09T16:05:36Z)
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.