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BDCC
Special Issues
Generative AI and Large Language Models
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Generative AI and Large Language Models

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 11423

Special Issue Editors

Dr. Fabrizio Marozzo

E-Mail Website
Guest Editor
Department of Informatics, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria, 87036 Rende, Italy
Interests: big data analysis; social media analysis; cloud computing; data mining; machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Cantini

E-Mail Website
Guest Editor
Department of Informatics, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria, 87036 Rende, Italy
Interests: big data analysis; social media analysis; deep learning; machine learning; generative AI

Special Issue Information

Dear Colleagues,

We are delighted to announce the introduction of a Special Issue in Big Data and Cognitive Computing (BDCC), dedicated to the exploration of Large Language Models (LLMs). In recent years, LLMs have emerged as transformative tools with the potential to revolutionize various aspects of language understanding, generation, and manipulation.

The advent of models like GPT-3 has opened up new possibilities for natural language processing, understanding, and generation on an unprecedented scale. LLMs, with their ability to comprehend context, learn patterns, and generate coherent text, have found applications in diverse domains, including but not limited to education, healthcare, content creation, and customer support.

This Special Issue aims to bring together researchers and practitioners to share their insights, findings, and advancements in the field of Large Language Models. We encourage the submission of original research papers and comprehensive reviews on topics related to LLMs, including but not limited to the following:

  • Novel architectures and training techniques;
  • Efficient training strategies for scaling up language models;
  • Applications of large language models;
  • Ethical considerations and bias mitigation;
  • Evaluation metrics and benchmarks.

We invite researchers, academics, and industry professionals to contribute to this Special Issue and help shape the future of Large Language Models. Submissions can include original research, case studies, and reviews that shed light on the challenges, advancements, and applications of LLMs.

We look forward to your valuable contributions and the collective advancement of knowledge in the exciting field of Large Language Models. 

Dr. Fabrizio Marozzo
Dr. Riccardo Cantini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • generative AI
  • large language models (LLMs)
  • natural language processing
  • deep learning
  • ChatGPT

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Published Papers (4 papers)

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Research

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20 pages, 3811 KiB  
Article
Health Use Cases of AI Chatbots: Identification and Analysis of ChatGPT Prompts in Social Media Discourses
by Amir Karami, Zhilei Qiao, Xiaoni Zhang, Hadi Kharrazi, Parisa Bozorgi and Ali Bozorgi
Big Data Cogn. Comput. 2024, 8(10), 130; https://doi.org/10.3390/bdcc8100130 - 8 Oct 2024
Cited by 1 | Viewed by 2760
Abstract
The popularity of ChatGPT has raised questions surrounding AI’s potential for health use cases. Since the release of ChatGPT in 2022, social media users have shared their prompts and ChatGPT responses on different topics such as health. Despite editorials and opinion articles discussing [...] Read more.
The popularity of ChatGPT has raised questions surrounding AI’s potential for health use cases. Since the release of ChatGPT in 2022, social media users have shared their prompts and ChatGPT responses on different topics such as health. Despite editorials and opinion articles discussing the potential uses of ChatGPT, there is a lack of a systematic approach to identify and analyze the use cases of ChatGPT in health. This study establishes a framework for gathering and identifying tweets (i.e., posts on social media site “X”, formerly known as Twitter) that discuss health use cases of ChatGPT, integrating topic modeling with constructivist grounded theory (CGT) to organize these topics into common categories. Using this framework, nine topics were identified, which were further grouped into four categories: (1) Clinical Workflow, (2) Wellness, (3), Diseases, and (4) Gender Identity. The Clinical Workflow category was the most popular category, and included four topics: (1) Seeking Advice, (2) Clinical Documentation, (3) Medical Diagnosis, and (4) Medical Treatment. Among the identified topics, “Diet and Workout Plans” was the most popular topic. This research highlights the potential of social media to identify the health use cases and potential health applications of an AI-based chatbot such as ChatGPT. The identified topics and categories can be beneficial for researchers, professionals, companies, and policymakers working on health use cases of AI chatbots. Full article
(This article belongs to the Special Issue Generative AI and Large Language Models)
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15 pages, 3809 KiB  
Article
QA-RAG: Exploring LLM Reliance on External Knowledge
by Aigerim Mansurova, Aiganym Mansurova and Aliya Nugumanova
Big Data Cogn. Comput. 2024, 8(9), 115; https://doi.org/10.3390/bdcc8090115 - 9 Sep 2024
Viewed by 3114
Abstract
Large language models (LLMs) can store factual knowledge within their parameters and have achieved superior results in question-answering tasks. However, challenges persist in providing provenance for their decisions and keeping their knowledge up to date. Some approaches aim to address these challenges by [...] Read more.
Large language models (LLMs) can store factual knowledge within their parameters and have achieved superior results in question-answering tasks. However, challenges persist in providing provenance for their decisions and keeping their knowledge up to date. Some approaches aim to address these challenges by combining external knowledge with parametric memory. In contrast, our proposed QA-RAG solution relies solely on the data stored within an external knowledge base, specifically a dense vector index database. In this paper, we compare RAG configurations using two LLMs—Llama 2b and 13b—systematically examining their performance in three key RAG capabilities: noise robustness, knowledge gap detection, and external truth integration. The evaluation reveals that while our approach achieves an accuracy of 83.3%, showcasing its effectiveness across all baselines, the model still struggles significantly in terms of external truth integration. These findings suggest that considerable work is still required to fully leverage RAG in question-answering tasks. Full article
(This article belongs to the Special Issue Generative AI and Large Language Models)
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17 pages, 1493 KiB  
Article
LLMs and NLP Models in Cryptocurrency Sentiment Analysis: A Comparative Classification Study
by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas and Dimitrios K. Nasiopoulos
Big Data Cogn. Comput. 2024, 8(6), 63; https://doi.org/10.3390/bdcc8060063 - 5 Jun 2024
Cited by 1 | Viewed by 3824
Abstract
Cryptocurrencies are becoming increasingly prominent in financial investments, with more investors diversifying their portfolios and individuals drawn to their ease of use and decentralized financial opportunities. However, this accessibility also brings significant risks and rewards, often influenced by news and the sentiments of [...] Read more.
Cryptocurrencies are becoming increasingly prominent in financial investments, with more investors diversifying their portfolios and individuals drawn to their ease of use and decentralized financial opportunities. However, this accessibility also brings significant risks and rewards, often influenced by news and the sentiments of crypto investors, known as crypto signals. This paper explores the capabilities of large language models (LLMs) and natural language processing (NLP) models in analyzing sentiment from cryptocurrency-related news articles. We fine-tune state-of-the-art models such as GPT-4, BERT, and FinBERT for this specific task, evaluating their performance and comparing their effectiveness in sentiment classification. By leveraging these advanced techniques, we aim to enhance the understanding of sentiment dynamics in the cryptocurrency market, providing insights that can inform investment decisions and risk management strategies. The outcomes of this comparative study contribute to the broader discourse on applying advanced NLP models to cryptocurrency sentiment analysis, with implications for both academic research and practical applications in financial markets. Full article
(This article belongs to the Special Issue Generative AI and Large Language Models)
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Review

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15 pages, 3243 KiB  
Review
A Review of Large Language Models in Healthcare: Taxonomy, Threats, Vulnerabilities, and Framework
by Rida Hamid and Sarfraz Brohi
Big Data Cogn. Comput. 2024, 8(11), 161; https://doi.org/10.3390/bdcc8110161 - 18 Nov 2024
Viewed by 848
Abstract
Due to the widespread acceptance of ChatGPT, implementing large language models (LLMs) in real-world applications has become an important research area. Such productisation of technologies allows the public to use AI without technical knowledge. LLMs can revolutionise and automate various healthcare processes, but [...] Read more.
Due to the widespread acceptance of ChatGPT, implementing large language models (LLMs) in real-world applications has become an important research area. Such productisation of technologies allows the public to use AI without technical knowledge. LLMs can revolutionise and automate various healthcare processes, but security is critical. If implemented in critical sectors such as healthcare, adversaries can manipulate the vulnerabilities present in such systems to perform malicious activities such as data exfiltration and manipulation, and the results can be devastating. While LLM implementation in healthcare has been discussed in numerous studies, threats and vulnerabilities identification in LLMs and their safe implementation in healthcare remain largely unexplored. Based on a comprehensive review, this study provides new findings which do not exist in the current literature. This research has proposed a taxonomy to explore LLM applications in healthcare, a threat model considering the vulnerabilities of LLMs which may affect their implementation in healthcare, and a security framework for the implementation of LLMs in healthcare and has identified future avenues of research in LLMs, cybersecurity, and healthcare. Full article
(This article belongs to the Special Issue Generative AI and Large Language Models)
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