A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

Ahmad Hammoud; Ahmed Alagha; Azzam Mourad; Chamseddine Talhi; Gordon Owusu Boateng; Hadi Otrok; Hanae Elmekki; Hani Sami; Jamal Bentahar; Mohsen Guizani

REVIEW 4 major objections 5 minor 250 references

A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A survey of 108 studies maps LLM use across four network management domains under one four-part taxonomy.

desk verdict A genuinely useful, broad-scope survey of LLMs for network/service management with a workable taxonomy and a disclosed PRISMA method, but the firstness claim outruns its own search procedure and the preprint exclusion could skew the corpus. read the letter →

arxiv 2412.19823 v1 pith:VPTFJEXU submitted 2024-12-16 cs.NI cs.AIcs.LG

Gordon Owusu Boateng , Hani Sami , Ahmed Alagha , Hanae Elmekki , Ahmad Hammoud , Rabeb Mizouni , Azzam Mourad , Hadi Otrok

show 5 more authors

Jamal Bentahar Sami Muhaidat Chamseddine Talhi Zbigniew Dziong Mohsen Guizani

This is my paper · ORCID

classification cs.NIcs.AIcs.LG

keywords largelanguagemodelsnetworkandservicemanagementcommunicationnetworksmobilevehicularcloudfogedgecomputingtaxonomy

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a survey, and its claim is about how the literature fits together. The authors set out to show that the scattered recent work on using large language models (LLMs) to manage communication networks forms a single landscape once it is seen through the right lens. They organize 108 selected studies under a four-part taxonomy — network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support — and they map each category onto four network domains: mobile networks and IoT, vehicular networks, cloud-based networks, and fog/edge-based networks. The paper also argues that this is the first survey to cover LLM-enabled network and service management (NSM) across all four domains at once, rather than focusing on a single domain such as telecom, transportation, or edge intelligence. If that claim is right, the contribution is a shared map: researchers can see which LLM-assisted NSM tasks are already studied, which domains are thin, and where to look for open problems.

What carries the argument

The organizing device is the taxonomy itself: a two-dimensional matrix whose rows are the four network domains (mobile/IoT, vehicular, cloud, fog/edge) and whose columns are the four NSM task families (monitoring and reporting, AI-powered planning, deployment and distribution, continuous support). The survey also relies on a systematic literature-selection process that starts with keyword searches, removes duplicates, screens titles, abstracts, and conclusions, excludes most preprints, and ends with 108 papers; this pipeline is what turns the taxonomy from an arbitrary scheme into a claimed mapping of the actual literature. The taxonomy does the argument's work: by placing each study in exactly one domain-task cell, it makes gaps, clusters, and the breadth of the 'first survey' claim visible.

What would settle it

Rerun the survey's search with the same databases and date cutoff but add synonyms the authors did not use, such as 'foundation model,' 'generative AI,' 'network operations,' and 'self-healing network,' and count how many additional peer-reviewed studies satisfy their inclusion criteria; a count large relative to the 108 included studies would undercut the claim of an extensive and representative map. Alternatively, locate a peer-reviewed survey published before December 2024 that already covers LLM-enabled NSM across all four network domains; one such survey would falsify the 'first' claim directly.

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Extended reading notes

Core claim

The central claim is that the body of research on LLMs for NSM can be comprehensively and usefully classified by crossing four network domains with four NSM task families. The paper states that it is, to its knowledge, the first extensive survey of LLM-enabled NSM spanning mobile networks and IoT technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. Under the proposed taxonomy, each application is categorized as monitoring and reporting, AI-powered network planning, network deployment and distribution, or continuous network support; the survey then reviews the selected papers within each cell, notes their methods and experimental results, and derives cross-cutting challenges and future directions. The authors' intended contribution is not a new algorithm or empirical result but a structured synthesis that reveals the state of the field and provides a roadmap for LLM-driven NSM.

Load-bearing premise

The load-bearing premise is that the keyword searches, abstract-level screening, and decision to exclude most preprints captured essentially all significant LLM-for-NSM research in the four domains; if important work was missed, the claimed first-mover status and the shape of the taxonomy could be misleading.

Editorial extensions

If this is right

Researchers working in one network domain gain a ready index of relevant LLM techniques and evaluation choices used in the other three domains.
The taxonomy exposes underserved combinations, such as continuous network support in fog/edge settings, as concrete targets for new work.
Practitioners can use the four task families to scope LLM deployments by matching a management need, such as intent-based configuration, to studies that already attempted it.
The survey's fundamentals section gives a common baseline for comparing general-purpose and domain-specific LLMs in NSM contexts.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The tabulated evidence suggests an uneven distribution: most existing studies concentrate on monitoring and detection, while continuous support and fog/edge deployment are comparatively thin; readers looking for open problems should start there.
Because the search cutoff is September 2024 and most preprints were excluded, the map is a snapshot; a regularly updated, openly maintained version of the domain-task matrix would extend its shelf life.
The same four-task taxonomy could be tested against foundation models that are not purely text-based, such as multimodal or vision-language models, to see whether the NSM task boundaries still hold.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This survey earns its place as a roadmap. It covers four network domains (mobile/IoT, vehicular, cloud, fog/edge) under one four-part taxonomy—monitoring/reporting, AI-powered planning, deployment/distribution, continuous support—and it documents 108 papers with tables that list contributions, datasets, and metrics. That is a real organizational contribution. The PRISMA flow and the explicit search strings make the selection process auditable, which is more than most surveys in this area bother to do. The LLM fundamentals section is competent and accessible.

The soft spots are real but not disqualifying. The “first extensive survey” claim in Section I-D is the weakest part. The paper compares itself to nine prior surveys, but its methodology does not include a systematic search for prior surveys or reviews, so the absence of a competing multi-domain survey is asserted rather than demonstrated. The authors can keep the claim if they soften it to “to the best of our knowledge after a systematic search for related surveys,” but as written it outruns the evidence. The preprint exclusion (“excluded most preprints to ensure quality control” in Section I-E4) is disclosed, but in a 2023–2024 area where influential work circulates as arXiv preprints, this biases the corpus toward published work and may miss task categories that only exist in the preprint literature. That is a limitation, not a fatal flaw, and it should be stated as one.

There are also citation and cross-reference errors—for example, Table VI attributes [142] to Gao et al. while the text cites [142] as Fontana et al., and [111] is likewise reused for two different works. These are fixable but need a careful pass. The survey also reports performance numbers from cited papers without critical validation, which is normal for a survey but means readers should treat those numbers as claims, not verified fact. The many self-citations from the same research groups are noticeable but not disqualifying; the authors are active in exactly this area.

Bottom line: this is a solid, useful survey for anyone entering LLM-for-NSM or looking for a structured map of the field. It deserves peer review and would benefit from a revision that tightens the firstness claim, addresses the preprint bias, and fixes the reference errors. I would cite it as a roadmap.

Referee Report

4 major / 5 minor

Summary. This paper surveys applications of Large Language Models (LLMs) to communication network and service management (NSM). It uses a PRISMA-based methodology to select 108 papers and organizes them under a four-part taxonomy: network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, across four network domains (mobile/IoT, vehicular, cloud, and fog/edge). The paper also provides a tutorial on LLM fundamentals, summarizes related work in Table I, and concludes with challenges and future directions. The central claim, stated in Section I-D, is that this is the first extensive survey covering LLM-enabled NSM across those four multi-domain communication network types.

Significance. If the coverage is as complete as claimed, this survey would be a useful entry point for researchers and practitioners working on LLM-based network management. The explicit PRISMA flow diagram, the disclosed keyword search list and access dates, and the cross-domain taxonomy are methodological strengths that are often missing from other surveys. The paper also offers structured tables of primary works, which are convenient for locating relevant papers by domain and task. However, the significance of the survey is contingent on two assumptions that are not fully established: the validity of the 'first extensive survey' claim, and the representativeness of a corpus that deliberately excludes most preprints.

major comments (4)

[Section I-D; Section I-E1] The 'first extensive survey' claim in Section I-D is not established by the methodology described in Section I-E. The PRISMA keyword search targets primary studies on LLMs for NSM, but the paper does not report a systematic search for prior surveys or reviews with comparable multi-domain scope, and Table I compares only nine selected surveys. To support the firstness claim, the authors should either perform and document a systematic survey-of-surveys search (including preprint servers and with explicit inclusion criteria) or revise the claim to a weaker statement that reflects the search actually conducted.
[Section I-E4; Fig. 2] The eligibility-stage removal of 'most preprints' (Section I-E4, Fig. 2) introduces a potential selection bias that is load-bearing for the survey's coverage claim. For a field like LLM-for-NSM, where a large share of 2023-2024 advances appears first as arXiv preprints, excluding most preprints at an access date of September 6-8, 2024 can systematically omit recent task categories and results. The authors should quantify how many preprints were retained versus excluded, justify the retention criteria, and add a limitation paragraph explaining the effect on taxonomic completeness.
[Table VI; Section V] Table VI contains citation and attribution errors that undermine the survey's reliability as a reference. The rows 'Gao et al. (2024) [142]' and 'Liu et al. (2024) [111]' are used for works that the text attributes to references [180] and [181], and the reference number [142] is already used for Fontana et al. in Section IV.B.1. In addition, the text after Section IV.D points to 'Table VI' when the mobile-network summary is Table V. The reference list and table cross-references should be audited and corrected.
[Section II.C; Table IV] Table IV mixes reported and author-estimated values without per-entry provenance. The asterisk note '[∗] Indicates estimated values' is insufficient because the reader cannot tell whether the 1.8T-token GPT-4 figure, the 15T-token LLaMA 3 figure, or the hardware entries come from the cited papers or from the authors' assumptions. Each estimated cell should be explicitly marked, and the table should state the source basis for the estimates.

minor comments (5)

[Section V.B.2] The heading reads 'Network nonfiguration' and should be 'Network configuration.'
[Section I-E4; Fig. 2] The phrase 'Excluded most preprints to ensure quality control' would benefit from a precise count of retained versus excluded preprints; the flow diagram currently reports only the combined removal of 49 articles.
[Table V] Several rows (e.g., Dandoush et al., Tong et al., Shao et al., Rong et al.) list 'LLM Solution' and 'Dataset Used' as '-'; consider replacing hyphens with 'Not specified' for clarity.
[Section VI.A.2] The discussion of MonitorAssistant and the anomaly-detection systems notes lack of public data for some works, but the work in [198] is described as having 'no comprehensive evaluation' without a concrete statement of what validation was performed; consider adding a short critical assessment of that work's evidence.
[Section VI.C.1] The Emergence pipeline (reference [122]) is described in detail, but no figure number is cited for Fig. 19; ensure all figures are referenced in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the survey's claims are descriptive and not reduced to their inputs.

full rationale

This paper is a literature survey, not a derivation. Its central claim is a hedged firstness statement ('To the best of our knowledge, this is the first extensive survey...'), which is a claim about the absence of prior multi-domain surveys rather than a result derived from its own inputs. The PRISMA methodology (Section I-E, Fig. 2) is a self-reported selection procedure; the exclusion of most preprints is a corpus-completeness limitation, but it does not make any survey statement equivalent to its inputs by construction. The four-part taxonomy is author-defined and used for organization; defining a taxonomy and then classifying papers under it is not circular because the taxonomy is not claimed to be empirically derived from the corpus in a way that would force the classifications. No equations, fitted parameters, or uniqueness theorems are invoked. If any cited works overlap with the authors, the survey's descriptive content is not load-bearing on those citations; the central organizing value is independent of any single cited result. Therefore no significant circularity is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities. The survey's conclusions rest on two domain assumptions: the selected literature is representative, and the four-category taxonomy is a meaningful partition. The survey also assumes that the reported results of the cited papers are accurately described.

assumptions (3)

domain assumption The selected 108 manuscripts after PRISMA screening are representative and comprehensive for LLM applications in the four network domains.
The survey's conclusions and taxonomy depend on the completeness of the search and screening, which is subjective and excluded most preprints (Section I-E4).
domain assumption The four-category taxonomy (network monitoring and reporting, AI-powered network planning, network deployment and distribution, continuous network support) is a meaningful and exhaustive partition of LLM-for-NSM work.
The classification is author-defined and no evidence is provided that the categories are natural or complete (Section III).
domain assumption The reported results and performance numbers in the cited papers are accurately described.
The survey trusts the original papers' claims, but the observed citation and author-name mismatches reduce confidence in this assumption.

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Cite this review

Pith. "Pith review of A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions." pith.science (2026). https://pith.science/paper/VPTFJEXU

@misc{pith2026241219823,
  author       = {Pith},
  title        = {Pith review of: A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VPTFJEXU}},
  note         = {Machine review of arXiv:2412.19823}
}

read the original abstract

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM.

Figures

Figures reproduced from arXiv: 2412.19823 by the authors.

**Figure 2.** PRISMA flow diagram. E. Survey Methodology This survey employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to conduct a rigorous, thorough, and fair literature review, as it ensures a systematic approach to identifying, screening, and selecting relevant studies [50]. The PRISMA framework helps to maintain a high level of quality and consistency throughout the literature… view at source ↗

**Figure 3.** Outline of survey [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (22 more)

**Figure 4.** Figure 4: Transformer architecture [48]. to generate context-aware representations, capturing relationships among tokens within the input sequence. The decoder module is structured similarly to the encoder module, with self-attention, feed-forward neural network, and normalizat…

**Figure 5.** Figure 5: A. LLM for Network Monitoring and Reporting LLMs can play a critical role in network monitoring and reporting by automating and enhancing the detection, analysis, and reporting of network conditions. In communication networks, LLMs can support a variety of monitoring …

**Figure 5.** Figure 5: Proposed taxonomy of LLMs for communication NSM. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

**Figure 6.** Figure 6: LLM for mobile networks and technologies-based NSM. [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]

**Figure 7.** Figure 7: Illustration of LLM-empowered IDS [134]. [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]

**Figure 8.** Figure 8: NI-testbed architecture with NFV-intent integrated [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]

**Figure 9.** Figure 9: The training pipeline of TelecomGPT framework [71]. [PITH_FULL_IMAGE:figures/full_fig_p022_9.png]

**Figure 10.** Figure 10: WirelessLLM framework for spectrum sensing with [PITH_FULL_IMAGE:figures/full_fig_p024_10.png]

**Figure 11.** Figure 11: Overview of existing domains of using LLM for vehicular networks. [PITH_FULL_IMAGE:figures/full_fig_p025_11.png]

**Figure 12.** Figure 12: CAN-LLAMA2 finetuning model [161]. in an unsupervised learning setting. During training, the CANBERT model randomly masks some of the CAN IDs in the input, with the aim of anticipating the original IDs that have been masked. Here, the model is only trained on normal …

**Figure 13.** Figure 13: Sample scenario using the TrafficLLM model [170]. [PITH_FULL_IMAGE:figures/full_fig_p027_13.png]

**Figure 14.** Figure 14: The overall framework of IoV-BERT-IDS [91]. [PITH_FULL_IMAGE:figures/full_fig_p027_14.png]

**Figure 15.** Figure 15: The MAIN architecture [180]. connections, with the aim of reducing latency and ensuring efficient communications. In this context, the researchers in [181] aimed to enhance Reconfigurable Intelligent Surfaces (RIS) using LLMs, aiming to achieve energy efficiency and r…

**Figure 16.** Figure 16: Feedback and refinement in the TrafficLLM method [170]. [PITH_FULL_IMAGE:figures/full_fig_p029_16.png]

**Figure 16.** Figure 16: The model is then continuously refined through more [PITH_FULL_IMAGE:figures/full_fig_p031_16.png]

**Figure 17.** Figure 17: Overview of existing domains of using LLM for cloud-based NSM [PITH_FULL_IMAGE:figures/full_fig_p032_17.png]

**Figure 18.** Figure 18: LLM system for decomposition and translation [202]. [PITH_FULL_IMAGE:figures/full_fig_p034_18.png]

**Figure 19.** Figure 19: LLM Pipeline for IBN deployment [122]. The evaluation uses OpenAI’s ChatGPT (GPT-3.5 and GPT-4) models trained with few-shot learning to classify, decompose, and validate intents into executable policies. The system was tested through intent fulfillment, where it gene…

**Figure 20.** Figure 20: The LLM-Twin framework [215]. data effectively. Additionally, limited integration of AI, particularly LLMs, means existing systems are often reactive rather than proactive. Future research should focus on leveraging LLMs to process real-time traffic, predict workload…

**Figure 21.** Figure 21: Overview of ADARMA framework [219]. repair tools. In the same context, Sarda et al. [219] proposed ADARMA, a pipeline to address runtime anomaly detection and autoremediation in microservice architectures using LLMs. The proposed system first detects anomalies in mic…

**Figure 22.** Figure 22: LLM deployment in edge networks. devices. In this setup, an LLM acts as a teacher, generating guidance prompts to help a smaller language model, which serves as the student and completes responses based on these prompts. This approach uses the strengths of larger mode…

**Figure 23.** Figure 23: Overview of GenG framework [227]. 2) Data generation: LLMs provide a promising solution for data generation, effectively addressing the challenges related to the high costs of obtaining high-quality real-world data, thus mitigating the problem of insufficient training…

Discussion (0). Continue with ORCID to comment.

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