Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications

• URL: http://arxiv.org/abs/2409.05314v2
• Date: Fri, 13 Sep 2024 23:56:00 GMT
• Title: Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications
• Authors: Ali Maatouk, Kenny Chirino Ampudia, Rex Ying, Leandros Tassiulas,
• Abstract summary: We develop and open-source Tele-LLMs, the first series of language models ranging from 1B to 8B parameters, specifically tailored for telecommunications. Our evaluations demonstrate that these models outperform their general-purpose counterparts on Tele-Eval while retaining their previously acquired capabilities.
• Score: 20.36003316123051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of large language models (LLMs) has significantly impacted various fields, from natural language processing to sectors like medicine and finance. However, despite their rapid proliferation, the applications of LLMs in telecommunications remain limited, often relying on general-purpose models that lack domain-specific specialization. This lack of specialization results in underperformance, particularly when dealing with telecommunications-specific technical terminology and their associated mathematical representations. This paper addresses this gap by first creating and disseminating Tele-Data, a comprehensive dataset of telecommunications material curated from relevant sources, and Tele-Eval, a large-scale question-and-answer dataset tailored to the domain. Through extensive experiments, we explore the most effective training techniques for adapting LLMs to the telecommunications domain, ranging from examining the division of expertise across various telecommunications aspects to employing parameter-efficient techniques. We also investigate how models of different sizes behave during adaptation and analyze the impact of their training data on this behavior. Leveraging these findings, we develop and open-source Tele-LLMs, the first series of language models ranging from 1B to 8B parameters, specifically tailored for telecommunications. Our evaluations demonstrate that these models outperform their general-purpose counterparts on Tele-Eval while retaining their previously acquired capabilities, thus avoiding the catastrophic forgetting phenomenon.

Related papers

• Telecom Foundation Models: Applications, Challenges, and Future Trends [0.5249805590164903]
  Foundation Models (FMs) show effective generalization capabilities in various domains in language, vision, and decision-making tasks. FMs can be trained on multiple data modalities generated from the telecom ecosystem and leverage specialized domain knowledge. This paper investigates the potential opportunities of using FMs to shape the future of telecom technologies and standards.
  arXiv  Detail & Related papers  (2024-08-02T21:09:13Z)
• Technical Language Processing for Telecommunications Specifications [0.0]
  Large Language Models (LLMs) are continuously being applied in a more diverse set of contexts. One such area with real-world technical documentation is telecommunications engineering. This article outlines the limitations of out-of-the-box NLP tools for processing technical information generated by telecommunications experts.
  arXiv  Detail & Related papers  (2024-06-04T13:57:22Z)
• WDMoE: Wireless Distributed Large Language Models with Mixture of Experts [65.57581050707738]
  We propose a wireless distributed Large Language Models (LLMs) paradigm based on Mixture of Experts (MoE) We decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at base station (BS) and mobile devices. We design an expert selection policy by taking into account both the performance of the model and the end-to-end latency.
  arXiv  Detail & Related papers  (2024-05-06T02:55:50Z)
• Unveiling the Generalization Power of Fine-Tuned Large Language Models [81.70754292058258]
  We investigate whether fine-tuning affects the intrinsic generalization ability intrinsic to Large Language Models (LLMs) Our main findings reveal that models fine-tuned on generation and classification tasks exhibit dissimilar behaviors in generalizing to different domains and tasks. We observe that integrating the in-context learning strategy during fine-tuning on generation tasks can enhance the model's generalization ability.
  arXiv  Detail & Related papers  (2024-03-14T08:18:59Z)
• EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data [67.8302955948861]
  Large Language Models (LLMs) pre-trained on massive corpora have exhibited remarkable performance on various NLP tasks. Applying these models to specific domains still poses significant challenges, such as lack of domain knowledge. We focus on domain-specific continual pre-training of LLMs using E-commerce domain as an exemplar.
  arXiv  Detail & Related papers  (2023-12-25T11:31:47Z)
• Domain Prompt Learning with Quaternion Networks [49.45309818782329]
  We propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability of Vision-Language Models to specialized domains. We present a hierarchical approach that generates vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features. Our proposed method achieves new state-of-the-art results in prompt learning.
  arXiv  Detail & Related papers  (2023-12-12T08:49:39Z)
• 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)
• Observations on LLMs for Telecom Domain: Capabilities and Limitations [1.8782750537161614]
  We analyze capabilities and limitations of incorporating such models in conversational interfaces for the telecommunication domain. We present a comparative analysis of the responses from such models for multiple use-cases. We believe this evaluation would provide useful insights to data scientists engaged in building customized conversational interfaces for domain-specific requirements.
  arXiv  Detail & Related papers  (2023-05-22T15:04:16Z)
• Federated Learning: A Signal Processing Perspective [144.63726413692876]
  Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local datasets, without explicitly exchanging the data. This article provides a unified systematic framework for federated learning in a manner that encapsulates and highlights the main challenges that are natural to treat using signal processing tools.
  arXiv  Detail & Related papers  (2021-03-31T15:14:39Z)

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.