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Advancements in Large Language Models Applied in Multidisciplinary Research Contexts
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Advancements in Large Language Models Applied in Multidisciplinary Research Contexts

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 1055

Special Issue Editors

Dr. Francisco De Arriba-Pérez

E-Mail Website
Guest Editor
Information Technologies Group, atlanTTic, University of Vigo, 36310 Vigo, Spain
Interests: artificial intelligence; natural language processing; computing systems design; real-time systems; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Silvia García-Méndez

E-Mail Website
Guest Editor
Information Technologies Group, atlanTTic, University of Vigo, 36310 Vigo, Spain
Interests: artificial intelligence; computational linguistics; machine learning; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We welcome submissions to this Special Issue of Applied Sciences entitled “Advancements in Large Language Models Applied in Multidisciplinary Research Contexts”. This Special Issue focuses on the rapidly evolving field of large language models (LLMs) and their transformative role in various scientific and applied domains.

Recent research on LLMs has demonstrated their ability to support and enhance work in diverse areas such as healthcare, education, engineering, social sciences, and the arts. These models are revolutionizing how knowledge is generated, processed, and interpreted, enabling new forms of collaboration and insight in multidisciplinary settings.

We encourage submissions that explore novel applications of LLMs in real-world environments, including, but not limited to, fine-tuning methods, domain-specific adaptations, and their integration into decision-support systems. Contributions addressing challenges such as model interpretability, ethical considerations, performance optimization, and cross-domain scalability are also very welcome.

This Special Issue aims to showcase state-of-the-art approaches and tools that push the boundaries of how LLMs can be effectively and responsibly used to solve complex problems across disciplines.

Dr. Francisco De Arriba-Pérez
Dr. Silvia García-Méndez
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • machine learning
  • deep learning
  • artificial intelligence
  • human-centered applications
  • large language models (LLMs)

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Published Papers (1 paper)

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Research

30 pages, 21387 KB  
Article
An Intelligent Docent System with a Small Large Language Model (sLLM) Based on Retrieval-Augmented Generation (RAG)
by Taemoon Jung and Inwhee Joe
Appl. Sci. 2025, 15(17), 9398; https://doi.org/10.3390/app15179398 - 27 Aug 2025
Viewed by 740
Abstract
This study designed and empirically evaluated a method to enhance information accessibility for museum and art gallery visitors using a small Large Language Model (sLLM) based on the Retrieval-Augmented Generation (RAG) framework. Over 199,000 exhibition descriptions were collected and refined, and a question-answering [...] Read more.
This study designed and empirically evaluated a method to enhance information accessibility for museum and art gallery visitors using a small Large Language Model (sLLM) based on the Retrieval-Augmented Generation (RAG) framework. Over 199,000 exhibition descriptions were collected and refined, and a question-answering dataset consisting of 102,000 pairs reflecting user personas was constructed to develop DocentGemma, a domain-optimized language model. This model was fine-tuned through Low-Rank Adaptation (LoRA) based on Google’s Gemma2-9B and integrated with FAISS and OpenSearch-based document retrieval systems within the LangChain framework. Performance evaluation was conducted using a dedicated Q&A benchmark for the docent domain, comparing the model against five commercial and open-source LLMs (including GPT-3.5 Turbo, LLaMA3.3-70B, and Gemma2-9B). DocentGemma achieved an accuracy of 85.55% and a perplexity of 3.78, demonstrating competitive performance in language generation and response accuracy within the domain-specific context. To enhance retrieval relevance, a Spatio-Contextual Retriever (SC-Retriever) was introduced, which combines semantic similarity and spatial proximity based on the user’s query and location. An ablation study confirmed that integrating both modalities improved retrieval quality, with the SC-Retriever achieving a recall@1 of 53.45% and a Mean Reciprocal Rank (MRR) of 68.12, representing a 17.5 20% gain in search accuracy compared to baseline models such as GTE and SpatialNN. System performance was further validated through field deployment at three major exhibition venues in Seoul (the Seoul History Museum, the Hwan-ki Museum, and the Hanseong Baekje Museum). A user test involving 110 participants indicated high response credibility and an average satisfaction score of 4.24. To ensure accessibility, the system supports various output formats, including multilingual speech and subtitles. This work illustrates a practical application of integrating LLM-based conversational capabilities into traditional docent services and suggests potential for further development toward location-aware interactive systems and AI-driven cultural content services. Full article
(This article belongs to the Special Issue Advancements in Large Language Models Applied in Multidisciplinary Research Contexts)
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