Generative AI and Large Language Models

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1459

Special Issue Editors


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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

Published Papers (1 paper)

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Research

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
Viewed by 1099
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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