Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Large language models have become the central engine of modern marketing management, this review argues.

desk verdict A sloppy narrative review whose load-bearing statistics are misattributed or unverifiable; desk-reject despite the plausibility of its topic. read the letter →

arxiv 2501.10685 v1 pith:7Q65WRJJ submitted 2025-01-18 cs.CL

Raha Aghaei , Ali A. Kiaei , Mahnaz Boush , Javad Vahidi , Mohammad Zavvar , Zeynab Barzegar , Mahan Rofoosheh This is my paper · ORCID
classification cs.CL
keywords largelanguagemodelsmarketingmanagementhyper-personalizationcontentautomationpredictiveanalyticscustomerengagementethicalAIsentimentanalysis
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 sets out to show that large language models have become the central engine of modern marketing management, reshaping customer engagement, campaign optimization, and content generation. It surveys applications from automated copywriting and personalized recommendations to sentiment analysis, chatbots, and predictive analytics, and it reports that businesses deploying LLMs see double-digit improvements in engagement, conversion, and response times. The review also catalogs barriers—computational cost, data privacy, bias, workforce skills—and closes with strategic recommendations for ethical and scalable adoption. For a marketer, the paper is a roadmap: adopt LLMs across the funnel, but govern them carefully.

What carries the argument

The central machinery is the transformer-based large language model: a neural architecture using self-attention to weigh the relevance of every word against every other word, pre-trained on massive text corpora and fine-tuned for specific marketing tasks. Self-attention captures long-range context that earlier recurrent networks missed, which is what lets one model generate ad copy, classify sentiment, recommend products, and converse with customers. Pre-training supplies general language competence; fine-tuning adapts the model to jobs like churn prediction or campaign messaging. In this review, the transformer's versatility is what carries the argument that a single technology can drive content, personalization, analytics, and conversation at once.

What would settle it

Check reference [29] (SOMONITOR) and reference [33] for the claimed 25% engagement increase and 40% response-time decrease; if those figures are absent, the paper's evidence base fails. A randomized field experiment comparing LLM-generated and human-written campaigns on a matched audience would independently settle whether the claimed engagement lift is real.

Watch

Extended reading notes

Core claim

The paper's core claim is that LLMs deliver measurable, cross-cutting gains across the marketing funnel, and that these gains are already visible in practice. In the paper's telling, LLM-generated content lifts engagement by 25%, AI chatbots cut response times by 40% and raise satisfaction by 30%, and predictive personalization increases customer retention by 39%. The same models enable real-time market analysis, sentiment tracking, and dynamic campaign adjustment, making one-to-one personalization scalable. The paper presents these figures as evidence that LLMs are now indispensable to marketing management, while insisting that responsible use requires ethical frameworks, bias mitigation, and human oversight.

Load-bearing premise

The paper's argument leans on the accuracy of the external statistics it quotes—a 25% rise in engagement, a 40% drop in response times, a 39% retention gain—yet many of those numbers are unverifiable or attached to references that do not report them.

Editorial extensions

If this is right

  • Content production shifts from human authoring to human-edited AI drafting, letting brands scale blogs, ads, and social posts while keeping a consistent voice.
  • Personalization becomes genuinely individual, with LLMs generating dynamic web pages, emails, and product recommendations tailored to each customer's behavior and context.
  • Marketing analytics moves from retrospective reporting to real-time, conversational insight, so teams can adjust campaigns mid-flight based on sentiment and trend signals.
  • Customer service response times and costs fall as LLM chatbots handle routine and many complex queries around the clock, with humans reserved for escalation.
  • Ethical AI policies, bias audits, and transparency mechanisms become standard practice for marketing teams deploying LLMs.

Reading between the lines

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

  • The effect sizes the paper repeats—25% engagement lift, 40% faster responses, 39% retention gain—are frequently attached to references that do not contain them; if those numbers cannot be traced, the strength of the review's evidence collapses even though the qualitative direction of the claims may still hold.
  • A randomized field experiment assigning customers to LLM-generated versus human-written campaigns would convert the paper's correlational anecdotes into causal evidence; this is the natural next step the paper does not propose.
  • The paper's structure implies a maturity model in which firms move from isolated content automation to integrated predictive personalization; that ladder could be operationalized as a readiness assessment for marketing teams.
  • Because the paper leans on vendor reports and unreviewed preprints, its statistics likely reflect optimistic selection; an independent meta-analysis of peer-reviewed LLM marketing studies would provide more reliable baselines.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This manuscript is a narrative review/survey of how large language models (LLMs) can be applied to marketing management. It covers personalization, content generation, customer engagement, market analysis, chatbots, campaign optimization, social media, ethics, challenges, and strategic recommendations, illustrated with several figures and numerous quantitative claims (e.g., 25% engagement increases, 40% response-time reductions, 35% sales lifts). The paper presents no original experiments, datasets, or case-study analyses of its own; its argument rests on citations to external sources and on figures that are asserted to show effect sizes. The conclusion recommends that marketers adopt LLM-based tools for personalization, automation, and predictive analytics while adhering to ethical frameworks.

Significance. The topic is timely and of practical interest, and the paper usefully catalogs a range of LLM applications in marketing. However, the significance is severely limited by the absence of any verifiable evidence base. The central claim that LLMs have 'revolutionized' marketing outcomes is supported almost entirely by numerical statistics attributed to sources that, on inspection, do not contain those statistics (e.g., §3-1 cites reference [29] for a 25% engagement rise; the cited SOMONITOR paper does not report such an experiment). The manuscript also contains unsourced figures (Figures 4, 5, 7, 9) and numerous unverifiable brand case studies. There is no reproducible code, no machine-checked derivation, and no original data. The paper's value as a strategic recommendation document is further undermined by pervasive text-quality issues, duplicated paragraphs, and references that are irrelevant or misattributed. For these reasons, the paper does not currently meet the standards of a scholarly contribution, although a rigorously sourced and methodologically transparent survey on this topic would be valuable.

major comments (4)
  1. [§3-1, §3-2, §5-2, §6-1] The manuscript's central quantitative claims are attributed to references that do not support them. For example, §3-1 states that 'businesses using LLMs for content production observed a 25% rise in engagement rates' and cites reference [29] (SOMONITOR, Farseev et al.), but that paper describes an explainable AI/LLM framework for marketing analytics and does not report a 25% engagement experiment. The same reference is later used for a 20% engagement increase (§3-2), a 35% sales increase (§6-1), a 70% inquiry-handling capacity increase (§5-2), and a 20% campaign-management improvement (§5-1). Because the paper contains no original measurements, these statistics are load-bearing for the claim that LLMs deliver the asserted business outcomes; without traceable sources, that claim is not established.
  2. [§1-2 and §4-1] Several quantitative claims are both implausible and unsupported. In §1-2 the authors write that engagement rates of businesses using LLM-generated content have been 'several orders of magnitude above traditional methods,' citing [29], which reports no such result and which would be an extraordinary effect that no controlled study in marketing supports. Similarly, §4-1 claims that automated LLM analysis reduces the time and effort needed for analysis 'by 99%,' with no citation or methodology. These and other unsourced numbers (e.g., the '39% increase in customer retention' in §3-2 and the '50% decrease in average response times' in §5-2) are presented as empirical facts. The authors should either replace them with verifiable, appropriately cited sources or substantially temper the claims.
  3. [Figures 4, 5, 7, 9] Several figures are presented as evidence supporting the paper's central effectiveness claims, but no data, methodology, or source is provided. Figure 4 is said to show 'a substantial increase in customer engagement, decreased marketing costs, and the sustained growth of ROI over time'; Figure 5 is said to show customer engagement increasing 'immediately after the usage of LLMs'; Figure 7 compares ROI with and without LLMs; and Figure 9 is a 'heatmap of LLMs' impact in marketing areas.' No description of how these figures were constructed, what datasets they use, or where the underlying measurements come from is given. If these are illustrative schematics, they should be labeled as such and not used to support quantitative conclusions; if they are based on real data, that data must be disclosed.
  4. [§10-1 and §10-2] The case studies of Coca-Cola, Amazon, Netflix, Nike, Spotify, Sephora, Starbucks, and Unilever report specific performance improvements (e.g., 30% engagement for Coca-Cola, 50% resolution-time reduction for Amazon, 40% retention for Netflix, 25% ROI for Unilever) with citations that are generic or unrelated. For instance, the Coca-Cola claim is cited to [33], a Semantic Scholar page on 'LLMs for Conversational AI,' which does not contain a Coca-Cola case study; the Netflix claim is cited to [77], an undergraduate-style report, not a primary source. No case-study methodology, dates, or link to original company disclosures is provided. These unverifiable success stories are central to the paper's recommendation that marketers adopt LLMs, so they need to be either properly sourced or removed.
minor comments (6)
  1. [Title and structure] The title promises 'Applications, Future Directions, and Strategic Recommendations,' which is fine, but the organization is repetitive: several sections begin with nearly identical sentences, and some headings are duplicated (e.g., 'Personalization and Customer Engagement' appears twice in §1-1, and §10-2 contains repeated bullet points with identical lead-ins).
  2. [§1 Introduction] The introduction includes a long block of references [1]–[23] that are almost entirely about the RAIN medical protocol and unrelated AI topics; these are irrelevant to marketing and should be removed.
  3. [§10-2] The heading 'Lesions Learned and Best Practices' contains a typo; it should be 'Lessons Learned and Best Practices.'
  4. [§4 opening] The first sentence of Section 4 ('Dalle 2 is the tech again Develops a hardware technology that cater for a what under of space called AI in marketing') is incoherent and appears to be a generation artifact; it should be rewritten or deleted.
  5. [§5-1 and elsewhere] There are many duplicated sentences and orphaned phrases, such as 'So, in short, what is one of the ways you can use NLG?' and 'Above all, Digital Twins fortify customer interaction,' which interrupt the narrative and should be removed.
  6. [References] Several references are incomplete or mislabeled. Reference [25] is a paper on class-balanced methods for long-tailed visual recognition, not a source on GPT-3; references [58] and [59] are identical; and several URLs point to Semantic Scholar or ResearchGate pages rather than to the actual cited reports. The reference list should be thoroughly cleaned and verified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a narrative review with no derived predictions or fitted parameters, and its self-citations are unrelated to the central marketing claims.

full rationale

This paper does not attempt a derivation chain: it contains no equations, no fitted parameters, no empirical model, and no quantity that is predicted from another quantity defined in the paper. Its central claims about LLM capabilities in marketing are supported by cited external studies and by narrative argument, not by any formal reduction from assumptions to conclusions. The self-citations that appear ([1] through [23]) concern the authors' RAIN protocol for drug-combination discovery in medicine and are introduced as examples of LLMs being used in other fields; they do not supply any premise from which the marketing conclusions are derived, so they are not load-bearing. The paper also repeatedly attributes quantitative statistics to reference [29] (SOMONITOR), and several of those attributed statistics do not appear in that cited paper; however, this is a citation-accuracy or evidence-support problem, not circular reasoning, because the statistics are presented as external empirical findings rather than as outputs of a model the paper itself defines. There is no step where an input is renamed as a prediction, no uniqueness theorem imported from the authors' own prior work, and no ansatz smuggled in through a self-citation. The dependence on external sources is a dependence on credibility, not a logical circle. Therefore the appropriate finding is no significant circularity, with score 0.

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

The paper's central claim depends on the trustworthiness of external statistics and citations. It also assumes that figures like Figure 4 reflect real data, and that the cited sources exist and support the claims. These are domain assumptions about the literature, not mathematical axioms.

assumptions (3)
  • domain assumption The external statistics cited (e.g., 25% engagement increase, 40% response time reduction) accurately represent real studies.
    The paper repeatedly cites specific percentages without providing raw data or methodology. These numbers are load-bearing for the conclusion that LLMs transform marketing. Many references (e.g., [29], [33]) do not appear to contain the attributed claims, so this assumption is unsafe.
  • ad hoc to paper The sources referenced in the bibliography exist and pertain to the claims made.
    Several references are links to Semantic Scholar or ResearchGate pages that may not support the specific statements. References [1]-[23] are self-citations to unrelated work on drug combinations and other topics, included without relevance to marketing.
  • ad hoc to paper The figures (Figure 4, Figure 5, Figure 7, Figure 9) are based on real data.
    The figures present trend lines and comparisons without any description of how they were constructed or what data they use. They appear illustrative rather than empirical.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations." pith.science (2026). https://pith.science/paper/7Q65WRJJ

@misc{pith2026250110685,
  author       = {Pith},
  title        = {Pith review of: Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7Q65WRJJ}},
  note         = {Machine review of arXiv:2501.10685}
}
read the original abstract

Large Language Models (LLMs) have revolutionized the process of customer engagement, campaign optimization, and content generation, in marketing management. In this paper, we explore the transformative potential of LLMs along with the current applications, future directions, and strategic recommendations for marketers. In particular, we focus on LLMs major business drivers such as personalization, real-time-interactive customer insights, and content automation, and how they enable customers and business outcomes. For instance, the ethical aspects of AI with respect to data privacy, transparency, and mitigation of bias are also covered, with the goal of promoting responsible use of the technology through best practices and the use of new technologies businesses can tap into the LLM potential, which help growth and stay one step ahead in the turmoil of digital marketing. This article is designed to give marketers the necessary guidance by using best industry practices to integrate these powerful LLMs into their marketing strategy and innovation without compromising on the ethos of their brand.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation

    cs.CY 2025-01 unverdicted

    A white paper summarizing potential LLM applications in supply chain management, without new experimental evidence.

Reference graph

Works this paper leans on

81 extracted references · 51 canonical work pages · cited by 1 Pith paper

  1. [46]

    Active Learning Literature Survey}

    “Active Learning Literature Survey}.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.researchgate.net/publication/228942691_Active_Learning_Literature_Survey

    work page arXiv 2025

  2. [33]

    LLMs for Conversational AI: Enhancing Chatbots and Virtual Assistants | Semantic Scholar

    “LLMs for Conversational AI: Enhancing Chatbots and Virtual Assistants | Semantic Scholar.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.semanticscholar.org/paper/LLMs-for-Conversational-AI%3A- Enhancing-Chatbots-and-Pappula-Allam/c0d13db7f22056294955466e450519f54ec051c4

    work page 2025

  3. [77]

    [PDF] A Study to Know - Use of AI For Personalized Recommendation, Streaming Optimization, and Original Content Production at Netflix | Semantic Scholar

    “[PDF] A Study to Know - Use of AI For Personalized Recommendation, Streaming Optimization, and Original Content Production at Netflix | Semantic Scholar.” Accessed: Jan. 03, 2025. [Online]. Available: https://www.semanticscholar.org/paper/A-Study-to-Know-Use-of-AI-For-Personalized-and-at- Khandelwal/9c8810f95c375c04ea83391c9c299f419f6d05b5

    work page 2025

  4. [1]

    Executive protocol designed for new review study called: Systematic Review and Artificial Intelligence Network Meta -Analysis (RAIN) with the first application for COVID-19,

    M. Mohammadi, N. Salari, A. H. Far, and A. Kiaei, “Executive protocol designed for new review study called: Systematic Review and Artificial Intelligence Network Meta -Analysis (RAIN) with the first application for COVID-19,” 2021, Accessed: Jul. 01, 2024. [Online]. Available: https://pure.northampton.ac.uk/files/31015475/CRD42021256797.pdf

    arXiv 2021

  5. [2]

    Identification of suitable drug combinations for treating COVID -19 using a novel machine learning approach: The RAIN method,

    A. Kiaei et al., “Identification of suitable drug combinations for treating COVID -19 using a novel machine learning approach: The RAIN method,” Life, vol. 12, no. 9, p. 1456, 2022

    2022

  6. [3]

    Emerging Drug Combinations for Targeting Tongue Neoplasms Associated Proteins/Genes: Employing Graph Neural Networks within the RAIN Protocol,

    M. Askari, A. A. Kiaei, M. Boush, and F. Aghaei, “Emerging Drug Combinations for Targeting Tongue Neoplasms Associated Proteins/Genes: Employing Graph Neural Networks within the RAIN Protocol,” bioRxiv, pp. 2024–06, 2024

    2024

  7. [4]

    AI -Enhanced RAIN Protocol: A Systematic Approach to Optimize Drug Combinations for Rectal Neoplasm Treatment,

    N. Dashti, A. A. Kiaei, M. Boush, B. Gholami -Borujeni, and A. Nazari, “AI -Enhanced RAIN Protocol: A Systematic Approach to Optimize Drug Combinations for Rectal Neoplasm Treatment,” bioRxiv, pp. 2024–05, 2024

    work page 2024

  8. [5]

    A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol,

    S. Sadeghi et al., “A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol,” medRxiv, pp. 2024–04, 2024

    2024

Show all 81 references
  1. [6]

    Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol,

    E. Parichehreh et al. , “Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol,” medRxiv, pp. 2024–02, 2024

    2024

  2. [7]

    D. Safaei et al., “Systematic review and network meta -analysis of drug combinations suggested by machine learning on genes and proteins, with the aim of improving the effectiveness of Ipilimumab in treating Melanoma,” medRxiv, pp. 2023–05, 2023

    2023

  3. [8]

    Boush, A

    M. Boush, A. A. Kiaei, D. Safaei, S. Abadijou, N. Salari, and M. Mohammadi, “Drug combinations proposed by machine learning on genes/proteins to improve the efficacy of Tecovirimat in the treatment of Monkeypox: A Systematic Review and Network Meta-analysis,” medRxiv, pp. 2023...

    2023

  4. [9]

    Recommending Drug Combinations using Reinforcement Learning to target Genes/proteins that cause Stroke: A comprehensive Systematic Review and Network Meta -analysis,

    M. Boush et al., “Recommending Drug Combinations using Reinforcement Learning to target Genes/proteins that cause Stroke: A comprehensive Systematic Review and Network Meta -analysis,” medRxiv, pp. 2023–04, 2023

    2023

  5. [10]

    Recommending Drug Combinations using Reinforcement Learning to target Genes/proteins that cause Stroke: A comprehensive Systematic Review and Network Meta -analysis,

    A. A. Kiaei, M. Boush, D. Safaei, S. Abadijou, N. Salari, and M. Mohammadi, “Recommending Drug Combinations using Reinforcement Learning to target Genes/proteins that cause Stroke: A comprehensive Systematic Review and Network Meta -analysis,” 2023, Ac cessed: Jul. 01, 2024. [...

    2023

  6. [11]

    A. A. Kiaei et al. , “Recommending Drug Combinations Using Reinforcement Learning targeting Genes/proteins associated with Heterozygous Familial Hypercholesterolemia: A comprehensive Systematic Review and Net -work Meta -analysis,” 2023, Accessed: Jul. 01, 2024. [Online]. Avai...

    2023

  7. [12]

    Trending Drugs Combination to Target Leukemia associated Proteins/Genes: using Graph Neural Networks under the RAIN Protocol,

    M. Boush, A. A. Kiaei, and H. Mahboubi, “Trending Drugs Combination to Target Leukemia associated Proteins/Genes: using Graph Neural Networks under the RAIN Protocol,” medRxiv, pp. 2023–08, 2023

    2023

  8. [13]

    Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID -19,

    N. Salari et al., “Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID -19,” Biol. Methods Protoc., vol. 8, no. 1, p. bpac038, 2023

    2023

  9. [14]

    DeePLT: personalized lighting facilitates by trajectory prediction of recognized residents in the smart home,

    D. Safaei, A. Sobhani, and A. A. Kiaei, “DeePLT: personalized lighting facilitates by trajectory prediction of recognized residents in the smart home,” Int. J. Inf. Technol., vol. 16, no. 5, pp. 2987–2999, Jun. 2024, doi: 10.1007/s41870-023-01665-1

    2024 doi

  10. [15]

    FPL: False Positive Loss,

    A. A. Kiaei et al. , “FPL: False Positive Loss,” 2023, Accessed: Jul. 01, 2024. [Online]. Available: https://www.preprints.org/manuscript/202309.0280

    2023

  11. [16]

    Active Identity Function as Activation Function,

    A. A. Kiaei et al. , “Active Identity Function as Activation Function,” 2023, Accessed: Jul. 01, 2024. [Online]. Available: https://www.preprints.org/manuscript/202305.1018

    2023

  12. [17]

    Autonomous Navigation of Wheeled Robot using a Deep Reinforcement Learning Based Approach,

    K. Dadashtabar Ahmadi, A. A. Kiaei, and M. A. Abbaszadeh, “Autonomous Navigation of Wheeled Robot using a Deep Reinforcement Learning Based Approach,” C4I J., vol. 6, no. 2, pp. 31–45, 2022

    2022

  13. [18]

    Fusion of medical images using Nabla operator; Objective evaluations and step-by-step statistical comparisons,

    Y. Behrouzi, A. Basiri, R. Pourgholi, and A. A. Kiaei, “Fusion of medical images using Nabla operator; Objective evaluations and step-by-step statistical comparisons,” Plos One, vol. 18, no. 8, p. e0284873, 2023

    2023

  14. [19]

    Diagnosing Alzheimer’s Disease Levels Using Machine Learning and MRI: A Novel Approach,

    R. Bahadori et al., “Diagnosing Alzheimer’s Disease Levels Using Machine Learning and MRI: A Novel Approach,” 2023, Accessed: Aug. 13, 2024. [Online]. Available: https://www.preprints.org/manuscript/202306.1184

    2023

  15. [20]

    The global prevalence of sexual dysfunction in women with multiple sclerosis: a systematic review and meta-analysis,

    N. Salari et al., “The global prevalence of sexual dysfunction in women with multiple sclerosis: a systematic review and meta-analysis,” Neurol. Sci., vol. 44, no. 1, pp. 59–66, Jan. 2023, doi: 10.1007/s10072-022-06406- z

    2023 doi

  16. [21]

    The effects of smoking on female sexual dysfunction: a systematic review and meta - analysis,

    N. Salari et al. , “The effects of smoking on female sexual dysfunction: a systematic review and meta - analysis,” Arch. Womens Ment. Health , vol. 25, no. 6, pp. 1021 –1027, Dec. 2022, doi: 10.1007/s00737 -022- 01281-1

    2022 doi

  17. [22]

    A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: The Ravansar county anthropometric cohort study,

    H. Jafari et al., “A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: The Ravansar county anthropometric cohort study,” PloS One, vol. 17, no. 1, p. e0262701, 2022

    2022

  18. [23]

    Global prevalence of osteoporosis among the world older adults: a comprehensive systematic review and analysis-meta

    “Global prevalence of osteoporosis among the world older adults: a comprehensive systematic review and analysis-meta .” Accessed: Jul. 01, 2024. [Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=fa&user=RDdZZwgAAAAJ&cstart=20&pa gesize=80&sortb...

    2024

  19. [24]

    Attention Is All You Need,

    A. Vaswani et al. , “Attention Is All You Need,” Aug. 02, 2023, arXiv: arXiv:1706.03762. doi: 10.48550/arXiv.1706.03762

    2023 doi arXiv

  20. [25]

    [PDF] Rethinking Class -Balanced Methods for Long -Tailed Visual Recognition From a Domain Adaptation Perspective | Semantic Scholar

    “[PDF] Rethinking Class -Balanced Methods for Long -Tailed Visual Recognition From a Domain Adaptation Perspective | Semantic Scholar.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.semanticscholar.org/paper/Rethinking-Class-Balanced-Methods-for-Long-Tailed-a-Jamal...

    2025

  21. [26]

    GPT-4 Technical Report

    “GPT-4 Technical Report.” Accessed: Jan. 02, 2025. [Online]. Available: https://arxiv.org/html/2303.08774v5

    2025 arXiv

  22. [27]

    MUM: A new AI milestone for understanding information

    “MUM: A new AI milestone for understanding information.” Accessed: Jan. 02, 2025. [Online]. Available: https://blog.google/products/search/introducing-mum/

    2025

  23. [28]

    [PDF] Language Models are Unsupervised Multitask Learners | Semantic Scholar

    “[PDF] Language Models are Unsupervised Multitask Learners | Semantic Scholar.” Accessed: Jan. 02,

  24. [30]

    Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3),

    T. Zhan, C. Shi, Y. Shi, H. Li, and Y. Lin, “Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3),” May 16, 2024, arXiv: arXiv:2405.09770. doi: 10.48550/arXiv.2405.09770

    2024 doi arXiv

  25. [31]

    The Netflix Recommender System: Algorithms, Business Value, and Innovation: ACM Transactions on Management Information Systems: Vol 6, No 4

    “The Netflix Recommender System: Algorithms, Business Value, and Innovation: ACM Transactions on Management Information Systems: Vol 6, No 4.” Accessed: Jan. 02, 2025. [Online]. Available: https://dl.acm.org/doi/10.1145/2843948

    2025 doi

  26. [32]

    Spotify expands AI Playlist feature to new markets including US, Canada | Reuters

    “Spotify expands AI Playlist feature to new markets including US, Canada | Reuters.” Accessed: Jan. 02,

  27. [34]

    Available: https://www.reuters.com/technology/artificial -intelligence/spotify-expands-ai- playlist-feature-new-markets-including-us-canada-2024-09-24/

    [Online]. Available: https://www.reuters.com/technology/artificial -intelligence/spotify-expands-ai- playlist-feature-new-markets-including-us-canada-2024-09-24/

    2024

  28. [35]

    [PDF] RoBERTa: A Robustly Optimized BERT Pretraining Approach | Semantic Scholar

    “[PDF] RoBERTa: A Robustly Optimized BERT Pretraining Approach | Semantic Scholar.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.semanticscholar.org/paper/RoBERTa%3A -A-Robustly- Optimized-BERT-Pretraining-Liu-Ott/077f8329a7b6fa3b7c877a57b81eb6c18b5f87de

    2025

  29. [36]

    (PDF) Leveraging AI Predictive Analytics for Marketing Strategy: The Mediating Role of Management Awareness

    “(PDF) Leveraging AI Predictive Analytics for Marketing Strategy: The Mediating Role of Management Awareness.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.researchgate.net/publication/378011631_Leveraging_AI_Predictive_Analytics_for_Marketing_S trategy_The_Mediat...

    2025

  30. [37]

    Large Language Models and Sentiment Analysis in Financial Markets: A Review, Datasets, and Case Study | IEEE Journals & Magazine | IEEE Xplore

    “Large Language Models and Sentiment Analysis in Financial Markets: A Review, Datasets, and Case Study | IEEE Journals & Magazine | IEEE Xplore.” Accessed: Jan. 02, 2025. [Online]. Available: https://ieeexplore.ieee.org/document/10638546

    2025

  31. [38]

    ALBERT: A Lite BERT for Self - supervised Learning of Language Representations,

    Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “ALBERT: A Lite BERT for Self - supervised Learning of Language Representations,” Feb. 09, 2020, arXiv: arXiv:1909.11942. doi: 10.48550/arXiv.1909.11942

    2020 doi arXiv

  32. [39]

    BERT: Pre -training of Deep Bidirectional Transformers for Language Understanding | Request PDF

    “BERT: Pre -training of Deep Bidirectional Transformers for Language Understanding | Request PDF.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.researchgate.net/publication/328230984_BERT_Pre- training_of_Deep_Bidirectional_Transformers_for_Language_Understanding

    2025

  33. [40]

    02, 2025

    “(PDF) PREDICTIVE ANALYTICS FOR CUSTOMER BEHAVIOUR: DEVELOPING A PREDICTIVE MODEL THAT ANALYZES CUSTOMER DATA TO FORECAST FUTURE BUYING TRENDS AND PREFERENCES, ENABLING SMALL BUSINESSES TO TAILOR THEIR MARKETING AND PRODUCT STRATEGIES EFFECTIVELY.” Ac cessed: Jan. 02, 2025. [O...

    2025

  34. [41]

    Longformer: The Long -Document Transformer

    “Longformer: The Long -Document Transformer.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.researchgate.net/publication/340598399_Longformer_The_Long-Document_Transformer

    2025

  35. [42]

    (PDF) Language Models are Few -Shot Learners

    “(PDF) Language Models are Few -Shot Learners.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.researchgate.net/publication/341724146_Language_Models_are_Few-Shot_Learners

    2025

  36. [43]

    Universal Language Model Fine -tuning for Text Classification,

    J. Howard and S. Ruder, “Universal Language Model Fine -tuning for Text Classification,” May 23, 2018, arXiv: arXiv:1801.06146. doi: 10.48550/arXiv.1801.06146

    2018 doi arXiv

  37. [44]

    Reformer: The Efficient Transformer,

    N. Kitaev, Ł. Kaiser, and A. Levskaya, “Reformer: The Efficient Transformer,” Feb. 18, 2020, arXiv: arXiv:2001.04451. doi: 10.48550/arXiv.2001.04451

    2020 doi arXiv

  38. [45]

    How Netflix Uses NLP for Show Recommendations

    “How Netflix Uses NLP for Show Recommendations.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.valuecoders.com/blog/ai-ml/how-netflix-uses-nlp-to-recommend-perfect-show-for-viewers/

    2025

  39. [47]

    Unveiling the Competitive Dynamics: A Comparative Evaluation of American and Chinese LLMs,

    Z. Jiang, J. Li, and Y. Liu, “Unveiling the Competitive Dynamics: A Comparative Evaluation of American and Chinese LLMs,” May 21, 2024, arXiv: arXiv:2405.06713. doi: 10.48550/arXiv.2405.06713

    2024 doi arXiv

  40. [48]

    SOMONITOR: Combining Explainable AI & Large Language Models for Marketing Analytics,

    A. Farseev, Q. Yang, M. Ongpin, I. Gossoudarev, Y.-Y. Chu-Farseeva, and S. Nikolenko, “SOMONITOR: Combining Explainable AI & Large Language Models for Marketing Analytics,” Dec. 10, 2024, arXiv: arXiv:2407.13117. doi: 10.48550/arXiv.2407.13117

    2024 doi arXiv

  41. [49]

    Top Strategic Technology Trends for 2022,

    “Top Strategic Technology Trends for 2022,” Gartner. Accessed: Jan. 02, 2025. [Online]. Available: https://www.gartner.com/en/documents/4006913

    2022

  42. [50]

    Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce,

    W. Xu, J. Xiao, and J. Chen, “Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce,” Oct. 02, 2024, arXiv: arXiv:2410.12829. doi: 10.48550/arXiv.2410.12829

    2024 doi arXiv

  43. [51]

    Revolutionizing Customer Service : The Impact of Large Language Models on Chatbot Performance | Semantic Scholar

    “Revolutionizing Customer Service : The Impact of Large Language Models on Chatbot Performance | Semantic Scholar.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.semanticscholar.org/paper/Revolutionizing-Customer-Service-%3A-The-Impact-of-on- Meduri/10d99208a5c9bfb...

    2025

  44. [52]

    Free Download: The State of Chatbots in B2B Demand Generation Report

    “Free Download: The State of Chatbots in B2B Demand Generation Report.” Accessed: Jan. 02, 2025. [Online]. Available: https://insights.botco.ai/free -download-report-the-state-of-chatbots-in-b2b-demand- generation

    2025

  45. [53]

    (PDF) Data-driven strategies for enhancing user engagement in digital platforms,

    “(PDF) Data-driven strategies for enhancing user engagement in digital platforms,” ResearchGate, Dec. 2024, doi: 10.51594/ijmer.v6i6.1170

    2024 doi

  46. [54]

    Leveraging GPT-4 Model on Root Cause Analysis through In-Context Learning

    “Leveraging GPT-4 Model on Root Cause Analysis through In-Context Learning.” Accessed: Jan. 02, 2025. [Online]. Available: https://arxiv.org/html/2401.13810v1?utm_source=chatgpt.com

    2025 arXiv

  47. [55]

    Building Brands through Social Listening,

    M. Rosenblatt, T. Curran, and J. Treiber, “Building Brands through Social Listening,” Northeast Bus. Econ. Assoc. NBEA 2018, Oct. 2018, [Online]. Available: https://digitalcommons.molloy.edu/bus_fac/41

    2018

  48. [56]

    Corporate Communication Companion (CCC): An LLM -empowered Writing Assistant for Workplace Social Media,

    Z. Lu, S. Mysore, T. Safavi, J. Neville, L. Yang, and M. Wan, “Corporate Communication Companion (CCC): An LLM -empowered Writing Assistant for Workplace Social Media,” May 07, 2024, arXiv: arXiv:2405.04656. doi: 10.48550/arXiv.2405.04656

    2024 doi arXiv

  49. [57]

    (PDF) Influencer Marketing ROI: Measurement Techniques and Optimization Strategies

    “(PDF) Influencer Marketing ROI: Measurement Techniques and Optimization Strategies.” Accessed: Jan. 02, 2025. [Online]. Available: https://www.researchgate.net/publication/383140060_Influencer_Marketing_ROI_Measurement_Techniques _and_Optimization_Strategies

    2025

  50. [58]

    Large Language Models for Social Networks: Applications, Challenges, and Solutions,

    J. Zeng et al., “Large Language Models for Social Networks: Applications, Challenges, and Solutions,” arXiv.org. Accessed: Jan. 02, 2025. [Online]. Available: https://arxiv.org/abs/2401.02575v1

    2025 arXiv

  51. [59]

    On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? ,

    E. M. Bender, T. Gebru, A. McMillan -Major, and S. Shmitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? ,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, in FAccT ’21. New York, NY, USA: Association for Co...

    2021

  52. [61]

    AI computing systems for LLMs training: a review,

    Z.-X. Zhang et al., “AI computing systems for LLMs training: a review,” J. Comput. Sci. Technol. , doi: 10.1007/s11390-024-4178-1

    doi

  53. [62]

    Towards Transparency by Design for Artificial Intelligence,

    H. Felzmann, E. Fosch-Villaronga, C. Lutz, and A. Tamò-Larrieux, “Towards Transparency by Design for Artificial Intelligence,” Sci. Eng. Ethics, vol. 26, no. 6, Art. no. 6, Nov. 2020, doi: 10.1007/s11948-020-00276- 4

    2020 doi

  54. [63]

    [2406.03777] Empirical Guidelines for Deploying LLMs onto Resource -constrained Edge Devices

    “[2406.03777] Empirical Guidelines for Deploying LLMs onto Resource -constrained Edge Devices.” Accessed: Jan. 03, 2025. [Online]. Available: https://arxiv.org/abs/2406.03777

    2025 arXiv

  55. [64]

    (PDF) Efficient LLMs Training and Inference: An Introduction

    “(PDF) Efficient LLMs Training and Inference: An Introduction.” Accessed: Jan. 03, 2025. [Online]. Available: https://www.researchgate.net/publication/385916997_Efficient_LLMs_Training_and_Inference_An_Introduc tion

    2025

  56. [65]

    Cloud-Native AI: Building and Deploying Large Language Models with Scalable Cloud Architectures | Integrated Journal of Science and Technology

    “Cloud-Native AI: Building and Deploying Large Language Models with Scalable Cloud Architectures | Integrated Journal of Science and Technology.” Accessed: Jan. 03, 2025. [Online]. Available: https://ijstindex.com/index.php/ijst/article/view/64

    2025

  57. [66]

    Understanding LLMs: A Comprehensive Overview from Training to Inference,

    Y. Liu et al., “Understanding LLMs: A Comprehensive Overview from Training to Inference,” Jan. 06, 2024, arXiv: arXiv:2401.02038. doi: 10.48550/arXiv.2401.02038

    2024 doi arXiv

  58. [67]

    [2304.13738] Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency

    “[2304.13738] Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency.” Accessed: Jan. 03, 2025. [Online]. Available: https://arxiv.org/abs/2304.13738

    2025 arXiv

  59. [68]

    Incentive approaches for cloud computing: challenges and solutions | Journal of Engineering and Applied Science | Full Text

    “Incentive approaches for cloud computing: challenges and solutions | Journal of Engineering and Applied Science | Full Text.” Accessed: Jan. 03, 2025. [Online]. Available: https://jeas.springeropen.com/articles/10.1186/s44147-024-00389-8

    2025 doi

  60. [69]

    Revolutionizing Marketing Research with a Large Language Model: A Hybrid AI -Human Approach by Neeraj Arora, Ishita Chakraborty, Yohei Nishimura :: SSRN

    “Revolutionizing Marketing Research with a Large Language Model: A Hybrid AI -Human Approach by Neeraj Arora, Ishita Chakraborty, Yohei Nishimura :: SSRN.” Accessed: Jan. 03, 2025. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4683054

    2025

  61. [70]

    The LLM Land Grab: How AWS, Azure, and GCP Are Sparring Over AI | Towards AI

    “The LLM Land Grab: How AWS, Azure, and GCP Are Sparring Over AI | Towards AI.” Accessed: Jan. 03, 2025. [Online]. Available: https://towardsai.net/p/cloud-computing/the-llm-land-grab-how-aws-azure-and- gcp-are-sparring-over-ai

    2025

  62. [71]

    When large language models meet personalization: perspectives of challenges and opportunities,

    J. Chen et al. , “When large language models meet personalization: perspectives of challenges and opportunities,” World Wide Web, vol. 27, no. 4, Art. no. 4, Jun. 2024, doi: 10.1007/s11280-024-01276-1

    2024 doi

  63. [72]

    ARTIFICIAL INTELLIGENCE AND HYPER -PERSONALIZATION FOR IMPROVING CUSTOMER EXPERIENCE,

    S. Maddodi, “ARTIFICIAL INTELLIGENCE AND HYPER -PERSONALIZATION FOR IMPROVING CUSTOMER EXPERIENCE,” Social Science Research Network, Rochester, NY: 3805904. Accessed: Jan. 03,

  64. [73]

    Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3805904

    [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3805904

  65. [74]

    Artificial intelligence and predictive marketing: an ethical framework from managers’ perspective,

    H. Naz and M. Kashif, “Artificial intelligence and predictive marketing: an ethical framework from managers’ perspective,” Feb. 2024, doi: 10.1108/SJME-06-2023-0154

    2024 doi

  66. [75]

    S. Kolasani, “Optimizing Natural Language Processing, Large Language Models (LLMs) for Efficient Customer Service, and hyper-personalization to enable sustainable growth and revenue | Kolasani | Transactions on Latest Trends in Artificial Intelligence ,” Apr. 2024, Accessed: J...

    2024

  67. [76]

    Ethical and legal challenges of AI in marketing: an exploration of solutions,

    D. Kumar and N. Suthar, “Ethical and legal challenges of AI in marketing: an exploration of solutions,” Jan. 2024, doi: 10.1108/JICES-05-2023-0068

    2024 doi

  68. [78]

    Leveraging Artificial Intelligence in Marketing for Social Good —An Ethical Perspective,

    E. Hermann, “Leveraging Artificial Intelligence in Marketing for Social Good —An Ethical Perspective,” J. Bus. Ethics, vol. 179, no. 1, Art. no. 1, May 2021, doi: 10.1007/s10551-021-04843-y

    2021 doi

  69. [79]

    Exploring the Ethical Use of Artificial Intelligence in Marketing and Advertising,

    B. K. Biswal, S. L. Choudhary, R. S. Dixit, P. Srivastava, and M. Kumar, “Exploring the Ethical Use of Artificial Intelligence in Marketing and Advertising,” J. Inform. Educ. Res., vol. 3, no. 2, Art. no. 2, Apr. 2023, doi: 10.52783/jier.v3i2.278

    2023 doi

  70. [81]

    “The role of explainability and transparency in fostering trust in AI healthcare systems: a systematic literature review, open issues and potential solutions | Neural Computing and Applications.” Accessed: Jan. 03,

  71. [82]

    Available: https://link.springer.com/article/10.1007/s00521-024-10868-x

    [Online]. Available: https://link.springer.com/article/10.1007/s00521-024-10868-x

    doi

  72. [83]

    How Explainability Contributes to Trust in AI,

    A. Ferrario and M. Loi, “How Explainability Contributes to Trust in AI,” Social Science Research Network, Rochester, NY : 4020557. Accessed: Jan. 03, 2025. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4020557

    2025

  73. [2025]

    Available: https://www.semanticscholar.org/paper/Language -Models-are-Unsupervised- Multitask-Learners-Radford-Wu/9405cc0d6169988371b2755e573cc28650d14dfe

    [Online]. Available: https://www.semanticscholar.org/paper/Language -Models-are-Unsupervised- Multitask-Learners-Radford-Wu/9405cc0d6169988371b2755e573cc28650d14dfe

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.