Submit to Special Issue Submit Abstract to Special Issue Review for Information Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advanced Retrieval-Augmented Generation Systems Based on Large Language Models

Print Special Issue Flyer
Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 22785

Share This Special Issue

Editors

Dr. Marina Bagic Babac

E-Mail Website
Guest Editor

Department of Applied Computing, University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia
Interests: artificial intelligence; natural language processing; machine learning

Dr. Andrea Giovanni Nuzzolese

E-Mail Website
Guest Editor

Institute for Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy
Interests: ontology engineering; linked data; semantic web; knowledge extraction; natural language understanding

Special Issue Information

Dear Colleagues,

We invite researchers, academics, and practitioners to contribute to this Special Issue focused on advanced retrieval-augmented generation (RAG) systems powered by large language models (LLMs). This issue aims to explore cutting-edge methodologies and solutions that leverage LLMs to enhance information retrieval, knowledge generation, and decision-making processes across various domains. Key topics include the following:

Advanced techniques for integrating LLMs into RAG systems to improve accuracy, efficiency, and scalability.
Cross-domain applications of RAG systems powered by LLMs, including healthcare, legal tech, education, and finance.
Novel approaches to fine-tuning LLMs for specialized retrieval tasks and domain-specific knowledge bases.
Comparative studies and evaluations of LLM-based RAG systems versus traditional or hybrid approaches.
Ethical, legal, and societal implications of deploying LLMs in RAG systems, including considerations of bias, fairness, and transparency.

We welcome submissions presenting original research, case studies, and theoretical advancements, addressing both technical and practical aspects of LLM-driven RAG systems.

Dr. Marina Bagic Babac
Dr. Andrea Giovanni Nuzzolese
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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

retrieval-augmented generation (RAG)
large language models (LLMs)
information retrieval
knowledge generation
decision making
cross-domain applications

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

44 pages, 4771 KB

Open AccessArticle

Evaluating LLM-Based Retrieval-Augmented Generation for Soil Science Question Answering

by Karla Topić, Marina Bagić Babac and Vedran Mornar

Information 2026, 17(9), 859; https://doi.org/10.3390/info17090859 - 4 Sep 2026

Viewed by 126

Abstract

Retrieval-augmented generation (RAG) systems for scientific literature require evidence-based choices of document segmentation, representation, retrieval, and generation components, particularly when the source collection varies in topical specificity and document structure. This study addresses the lack of an end-to-end, component-level comparison of these choices for soil science question answering. A three-stage evaluation was conducted across general, domain-specific, and geospatial soil science corpora. The corpus combines foundational soil science books, peer-reviewed research articles, European soil monitoring material, and geospatial mapping publications, thereby covering both broad disciplinary concepts and specialized scientific evidence. The study compares four chunking strategies, three embedding models, five retrieval methods, and five large language models. In Experiment 1, semantic chunking with text-embedding-3-large achieved the highest aggregate retrieval scores (recall@1 = 0.824; MRR = 0.819), whereas text-embedding-3-small delivered practically comparable performance at lower cost. In Experiment 2, hybrid reciprocal rank fusion achieved recall@5 values of 0.957, 0.960, and 0.647 for the general, domain-specific, and geospatial corpora, respectively; the cross-encoder reranker showed weaker rank quality on scientific content. In Experiment 3, model responses attained BERTScore values of 0.909–0.927 and faithfulness of at least 0.993; these automated measures indicate low contradiction with retrieved context but do not establish answer completeness or human-perceived correctness. The study provides a reproducible component-level evaluation design, characterizes the effect of corpus specificity on RAG retrieval, and identifies a practical configuration for soil science literature retrieval. Among the models retained for direct aggregate comparison, Llama 3.1 8B offered the most favorable observed balance of answer quality, latency, cost, and model openness. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Graphical abstract

28 pages, 749 KB

Open AccessArticle

The Grounded-but-Wrong Gap in Cybersecurity RAG: Evidence from a Bilingual Benchmark and Multi-Judge Evaluation

by Ema Pandilova, Marko Petrov, Ivan Chorbev, Dejan Gjorgjevikj and Dimitar Trajanov

Information 2026, 17(8), 790; https://doi.org/10.3390/info17080790 - 17 Aug 2026

Viewed by 361

Abstract

Retrieval-augmented generation (RAG) is increasingly deployed in safety-critical cybersecurity question answering, where an answer must be both factually correct and faithful to its sources. Evaluation in this space routinely conflates the two, treating grounding as a proxy for correctness. We show that this conflation is unsafe. We introduce SENTINEL-QA, a bilingual English and Macedonian cybersecurity QA benchmark of 70 hand-authored items over a curated corpus of authoritative source documents, and use it to compare four answer generation pipelines (zero-shot, Classic RAG, DSPy-structured RAG, and GraphRAG) across three generators, judged by a four-model LLM panel on both factual correctness and grounding. The two metrics decouple under graph-based retrieval augmentation: on all three generators our chunk-similarity GraphRAG makes answers look better sourced without making them more correct, and as the retrieval budget grows it can even degrade factual correctness while grounding holds steady. Structured, minimal prompting improves both qualities, at lower latency and cost; a four-cell ablation attributes the factual gain to pruning the instruction, while the declarative schema contributes on grounding. The judge panel mirrors the split, agreeing more on what is well grounded than on what is true, under absolute as well as rank-based agreement criteria. We conclude that grounding alone is an insufficient target for RAG evaluation and that pruning the instruction, rather than expanding retrieval, is what improves factual correctness, without eliminating the grounded-but-wrong regime. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Figure 1

30 pages, 502 KB

Open AccessArticle

S-Gens: Structure-Aware Synthetic Data Generation for Enhancing Reasoning-Intensive Dense Retrieval

by Zhou Lei, Yanqi Xu and Shengbo Chen

Information 2026, 17(5), 413; https://doi.org/10.3390/info17050413 - 26 Apr 2026

Viewed by 546

Abstract

Dense retrievers rely heavily on high-quality training triplets, yet existing data construction strategies remain inadequate for reasoning-intensive retrieval tasks involving multi-hop reasoning, entity relation tracing, and implicit evidence composition. Positive samples are often based on shallow semantic relevance and fail to capture explicit reasoning chains, while negative samples are typically sampled from lexical overlap or random candidates and therefore provide limited supervision for learning clear decision boundaries. To address these issues, we propose S-Gens, a structure-aware synthetic data generation framework for enhancing reasoning-intensive dense retrieval. S-Gens uses relation paths in an external knowledge graph to synthesize queries and structurally consistent positive samples, and further constructs semantically similar but structurally inconsistent hard negatives. To improve data reliability, we introduce a Siamese graph neural network-based consistency filtering mechanism. Because S-Gens operates entirely during offline supervision construction, it remains model-agnostic, preserves the original inference architecture, and is complementary to graph-guided retrieval or RAG pipelines that inject structure online. Experiments on five benchmark datasets show that S-Gens consistently improves multiple trainable retrievers, with the largest gains on multi-hop reasoning tasks such as WebQSP and HotpotQA. These results indicate that structure-aware synthetic supervision can effectively improve dense retrieval in reasoning-intensive settings. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Figure 1

17 pages, 913 KB

Open AccessArticle

An Empirical Study of Knowledge Graph-Enhanced RAG for Information Security Compliance

by Dimitar Jovanovski, Marija Stojcheva, Mila Dodevska, Petre Lameski, Igor Mishkovski and Dejan Gjorgjevikj

Information 2026, 17(4), 389; https://doi.org/10.3390/info17040389 - 20 Apr 2026

Cited by 2 | Viewed by 2520

Abstract

Information security compliance has become critical for organizations worldwide, with the ISO/IEC 27000 family serving as the most widely adopted framework for establishing information security management systems. Despite their global acceptance, these standards present significant interpretation challenges due to their formal language, abstract structure, and extensive cross-referencing across 97 documents. Traditional retrieval-augmented generation (RAG) systems, which rely on independent text chunking and dense vector retrieval, prove inadequate for such highly interconnected regulatory materials, often fragmenting contextual relationships and reducing accuracy. This study introduces a privacy-preserving RAG framework that integrates LightRAG, a knowledge graph-based retrieval system, with locally hosted open-source language models. Unlike chunk-based RAG systems that treat document segments independently, the system in this study constructs a semantic knowledge graph that explicitly models relationships between clauses through typed edges representing cross-references, semantic similarity, and hierarchical dependencies. To enable rigorous evaluation, we developed a curated benchmark dataset of 222 multiple-choice questions with authoritative ground-truth answers, systematically constructed from official ISO standards, certification preparation materials, and academic sources. Through systematic evaluation on this benchmark, we show that knowledge graph-based retrieval achieves higher accuracy than chunk-based RAG and non-retrieval LLM baselines within the evaluated setup. The analysis indicates that embedding model quality is strongly associated with system performance, that hybrid retrieval modes combining local and global graph traversal tend to yield better accuracy, and that mid-sized open-source models paired with strong retrievers can approach the performance of larger proprietary systems. The best configuration achieves 90.54% accuracy, demonstrating the promising effectiveness of graph-structured retrieval for multiple-choice regulatory questions. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Figure 1

25 pages, 7202 KB

Open AccessArticle

FusionGraphRAG: An Adaptive Retrieval-Augmented Generation Framework for Complex Disease Management in the Elderly

by Shaofu Lin, Shengze Shao, Xiliang Liu and Haoru Su

Information 2026, 17(2), 138; https://doi.org/10.3390/info17020138 - 1 Feb 2026

Cited by 2 | Viewed by 1800

Abstract

Elderly patients often experience multimorbidity and long-term polypharmacy, making medication safety a critical challenge in disease management. In China, the concurrent use of Western medicines and proprietary Chinese medicines (PCMs) further complicates this issue, as potential drug interactions are often implicit, increasing risks for physiologically vulnerable older adults. Although large language model-based medical question-answering systems have been widely adopted, they remain prone to unsafe outputs in medication-related contexts. Existing retrieval-augmented generation (RAG) frameworks typically rely on static retrieval strategies, limiting their ability to appropriately allocate retrieval and verification efforts across different question types. This paper proposes FusionGraphRAG, an adaptive RAG framework for geriatric disease management. The framework employs query classification-based routing to distinguish questions by complexity and medication relevance; integrates dual-granularity knowledge alignment to connect fine-grained medical entities with higher-level contextual knowledge across diseases, medications, and lifestyle guidance; and incorporates explicit contradiction detection for high-risk medication scenarios. Experiments on the GeriatricHealthQA dataset (derived from the Huatuo corpus) indicate that FusionGraphRAG achieves a Safety Recall of 71.7%. Comparative analysis demonstrates that the framework improves retrieval accuracy and risk interception capabilities compared to existing graph-enhanced baselines, particularly in identifying implicit pharmacological conflicts. The results indicate that the framework supports more reliable geriatric medical question answering while providing enhanced safety verification for medication-related reasoning. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Figure 1

23 pages, 1237 KB

Open AccessArticle

Enhancing Medical Question Answering with LLMs via a Hybrid Retrieval-Augmented Generation Framework

by Bushra Aljohani and Tawfeeq Alsanoosy

Information 2026, 17(2), 133; https://doi.org/10.3390/info17020133 - 1 Feb 2026

Cited by 3 | Viewed by 2984

Abstract

Given the knowledge-intensive and rapidly expanding nature of medical field, accurately synthesizing and interpreting findings remain a major challenge for clinicians and medical students. Although Large Language Models (LLMs) have advanced automated summarization or generated responses, their deployment is limited by hallucinations, outdated knowledge, and insufficient domain adaptation. Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLMs in external knowledge bases. However, as the document corpus scales, maintaining RAG accuracy becomes increasingly difficult, making retrievers critical for contextual relevance. In this paper, we examined the efficiency of a modular RAG framework with a hybrid retrieval strategy that combines sparse retrieval (BM25) and dense retrieval (MedCPT) to extract the most relevant documents from the corpus, thereby providing contextual grounding for the LLM to improve medical responses. Evaluation was conducted on three benchmark healthcare datasets: PubMedQA, MedMCQA, and MedQA-US, using two LLMs, GPT-4o and BioGPT. Performance was assessed using retrieval metrics (context precision, context recall, F1-score) and generation metrics (BERTScore, RAG Assessment Score). The hybrid retriever achieved 92.14% recall, 74.36% precision, and an F1-score of 82.30%. GPT-4o with hybrid retrieval reached 89.4% faithfulness, 82.7% answer relevancy, and an

{F 1}_{BERT}

of 88.0% on PubMedQA. Results demonstrated that hybrid retrieval within a modular architecture substantially improves retrieval effectiveness and response quality. The proposed work offers a scalable, generalizable solution for high-stakes healthcare applications, supporting flexible retriever integration and robust evaluation to advance transparent QA systems. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Figure 1

34 pages, 3333 KB

Open AccessArticle

A Systematic Evaluation of Large Language Models and Retrieval-Augmented Generation for the Task of Kazakh Question Answering

by Aigerim Mansurova, Arailym Tleubayeva, Aliya Nugumanova, Adai Shomanov and Sadi Evren Seker

Information 2025, 16(11), 943; https://doi.org/10.3390/info16110943 - 30 Oct 2025

Cited by 9 | Viewed by 5461

Abstract

This paper presents a systematic evaluation of large language models (LLMs) and retrieval-augmented generation (RAG) approaches for question answering (QA) in the low-resource Kazakh language. We assess the performance of existing proprietary (GPT-4o, Gemini 2.5-flash) and open-source Kazakh-oriented models (KazLLM-8B, Sherkala-8B, Irbis-7B) across closed-book and RAG settings. Within a three-stage evaluation framework we benchmark retriever quality, examine LLM abilities such as knowledge-gap detection, external truth integration and context grounding, and measures gains from realistic end-to-end RAG pipelines. Our results show a clear pattern: proprietary models lead in closed-book QA, but RAG narrows the gap substantially. Under the Ideal RAG setting, KazLLM-8B improves from its closed-book baseline of 0.427 to reach answer correctness of 0.867, closely matching GPT-4o’s score of 0.869. In the end-to-end RAG setup, KazLLM-8B paired with Snowflake retriever achieved answer correctness up to 0.754, surpassing GPT-4o’s best score of 0.632. Despite improvements, RAG outcomes show an inconsistency: high retrieval metrics do not guarantee high QA system accuracy. The findings highlight the importance of retrievers and context grounding strategies in enabling open-source Kazakh models to deliver competitive QA performance in a low-resource setting. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Graphical abstract

16 pages, 2128 KB

Open AccessArticle

Secure Multifaceted-RAG: Hybrid Knowledge Retrieval with Security Filtering

by Grace Byun, Shinsun Lee, Nayoung Choi and Jinho D. Choi

Information 2025, 16(9), 804; https://doi.org/10.3390/info16090804 - 16 Sep 2025

Cited by 10 | Viewed by 3104

Abstract

Existing Retrieval-Augmented Generation (RAG) systems face challenges in enterprise settings due to limited retrieval scope and data security risks. When relevant internal documents are unavailable, the system struggles to generate accurate and complete responses. Additionally, using closed-source Large Language Models (LLMs) raises concerns about exposing proprietary information. To address these issues, we propose the Secure Multifaceted-RAG (SecMulti-RAG) framework, which retrieves not only from internal documents but also from two supplementary sources: pre-generated expert knowledge for anticipated queries and on-demand external LLM-generated knowledge. To mitigate security risks, we adopt a local open-source generator and selectively utilize external LLMs only when prompts are deemed safe by a filtering mechanism. This approach enhances completeness, prevents data leakage, and reduces costs. In our evaluation on a report generation task in the automotive industry, SecMulti-RAG significantly outperforms traditional RAG—achieving 79.3–91.9% win rates across correctness, richness, and helpfulness in LLM-based evaluation and 56.3–70.4% in human evaluation. This highlights SecMulti-RAG as a practical and secure solution for enterprise RAG. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Figure 1

24 pages, 3421 KB

Open AccessArticle

Cloud-Based Medical Named Entity Recognition: A FIT4NER-Based Approach

by Philippe Tamla, Florian Freund and Matthias Hemmje

Information 2025, 16(5), 395; https://doi.org/10.3390/info16050395 - 12 May 2025

Cited by 1 | Viewed by 1882

Abstract

This paper presents a cloud-based system that builds upon the FIT4NER framework to support medical experts in training machine learning models for named entity recognition (NER) using Microsoft Azure. The system is designed to simplify complex cloud configurations while providing an intuitive interface for managing and converting large-scale training and evaluation datasets across formats such as PDF, DOCX, TXT, BioC, spaCyJSON, and CoNLL-2003. It also enables the configuration of transformer-based spaCy pipelines and orchestrates Azure cloud services for scalable and efficient NER model training. Following the structured Nunamaker research methodology, the paper introduces the research context, surveys the state of the art, and highlights key challenges faced by medical professionals in cloud-based NER. It then details the modeling, implementation, and integration of the system. Evaluation results—both qualitative and quantitative—demonstrate enhanced usability, scalability, and accessibility for non-technical users in medical domains. The paper concludes with insights gained and outlines directions for future work. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Figure 1

Review

Jump to: Research

43 pages, 2468 KB

Open AccessReview

Retrieval-Augmented Generation for Curated Thematic Corpora: A Critical Survey, Bibliometric Evidence, and the ThemePath-RAG Framework

by Winda Monika, Deshinta Arrova Dewi, Arbi Haza Nasution, Aytuğ Onan and Yohei Murakami

Information 2026, 17(7), 660; https://doi.org/10.3390/info17070660 - 7 Jul 2026

Cited by 1 | Viewed by 1750

Abstract

Retrieval-Augmented Generation (RAG) grounds large language models in external evidence, but many RAG systems represent knowledge either as flat text chunks or as automatically constructed indexing graphs. This assumption is incomplete for curated thematic corpora, including religious scriptures, legal codes, clinical guidelines, educational taxonomies, policy documents, and library classification systems, where domain experts have already organized knowledge into thematic paths and citeable canonical units. This paper investigates how RAG can exploit such expert-authored structures while pruning evidence to a compact and query-specific set. We conduct a critical survey supported by a bibliometric analysis of 2815 Scopus-indexed RAG-related records exported on 26 May 2026, of which 2809 records were retained after duplicate removal. The bibliometric results indicate rapid growth in RAG research but limited explicit consolidation around curated thematic paths, canonical evidence units, or thematic path-guided evidence pruning. We therefore propose ThemePath-RAG, a retrieval framework that retrieves curated thematic paths as high-recall semantic routes, expands candidate canonical evidence, and applies query-aware scoring and global pruning before generation. To assess operational feasibility, we implement ThemePath-RAG for Qur’anic question answering and compare it with a Vector RAG baseline on 150 paired questions using RAGAS context relevance with gpt-4o-mini as the LLM evaluator. Both methods return approximately three final ayat per question. Vector RAG achieves higher mean context relevance than ThemePath-RAG (

0.920

versus

0.798

;

p < 0.001

). Thus, the proof of concept establishes the feasibility of thematic-path-guided retrieval and identifies evidence-selection challenges, rather than demonstrating superiority over conventional vector retrieval. The paper clarifies the framework’s relationship to GraphRAG, LightRAG, HippoRAG, PathRAG, ontology-based RAG, and AI-augmented bibliometric systems, and outlines a language-matched, multi-baseline evaluation agenda for future cross-domain validation. Full article

(This article belongs to the Special Issue Advanced Retrieval-Augmented Generation Systems Based on Large Language Models)

► Show Figures

Journal Menu

Journal Browser

Advanced Retrieval-Augmented Generation Systems Based on Large Language Models

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI