Exploring the Synergy Between Large Language Models and Retrieval-Augmented Generation in Natural Language Processing, Human–Robot Interaction and Quantum Computing

Dear Colleagues,

Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have transformed Natural Language Processing and adjacent fields. This Special Issue explores the synergistic relationship between these technologies across three key domains—Natural Language Processing, Human–Robot Interaction, and Quantum Computing—paying particular attention to evaluation methodologies, reliability assessment, explainability, and knowledge graph integration.

LLMs have demonstrated remarkable capabilities in understanding and generating human-like text but face limitations, including hallucinations, knowledge cutoffs, and contextual constraints. RAG has emerged as a powerful framework that enhances LLMs by dynamically retrieving relevant external information—often presented as knowledge graphs—to ground model outputs in factual knowledge. This integration represents a significant frontier in AI research, combining the generative power of LLMs with the precision of retrieval systems.

In Natural Language Processing, RAG addresses the fundamental challenges of achieving factuality and up-to-date knowledge, enabling more reliable and contextually appropriate text generation with improved explainability. Knowledge graphs serve as structured repositories that can be queried to verify and supplement LLM outputs. In Human–Robot Interaction, LLM-RAG systems are revolutionizing how robots understand and respond to natural language instructions. These "agentic computing" systems enable robots to process open-ended commands, execute multi-step processes, and function more intuitively. However, challenges remain in grounding robot actions in the physical world, mitigating potentially unsafe behaviors caused by hallucinations, and developing robust error-handling mechanisms that ensure reliability.

Quantum Computing offers transformative opportunities for enhancing RAG systems, particularly in vector search optimization for information retrieval. The computationally intensive similarity calculations critical to document retrieval represent a bottleneck for large-scale implementations. Quantum-enhanced information retrieval could potentially revolutionize the efficiency of these operations. Additionally, RAG-enhanced LLMs show significant promise for quantum code generation, making quantum programming more accessible to non-specialists by providing contextually relevant code examples and documentation.

This Special Issue will supplement the existing literature by bridging these rapidly evolving fields, which have primarily been studied in isolation. While substantial research exists on LLMs, knowledge graphs, RAG, and their individual applications, their synergistic integration—especially in specialized domains like Human–Robot Interaction and Quantum Computing—remains underexplored. By fostering interdisciplinary dialogue, this Special Issue will accelerate innovation in areas where traditional approaches face limitations, placing a particular emphasis on evaluation methodologies and explainability to ensure these advanced systems are not only powerful but also trustworthy and interpretable.

We welcome the submission of original research papers, reviews, and case studies investigating theoretical foundations, methodological innovations, practical implementations, and ethical considerations in this emerging technology ecosystem. By bringing together diverse perspectives and cutting-edge research, we aim to advance our understanding of how these technologies can be effectively integrated to develop more capable, reliable, and trustworthy AI systems.

The topics of interest include but are not limited to the following:

RAG Architectures and Foundations:

- Novel RAG architectures and retrieval mechanisms optimized for various LLM frameworks.

- The integration of knowledge graphs with LLMs for enhanced retrieval and reasoning.

- Techniques for improving retrieval quality, relevance assessment, and contextual integration.

- Approaches to knowledge management, updates, and verification in RAG systems.

- Strategies for handling multi-modal information retrieval and generation.

Evaluation, Reliability, and Explainability:

- Methods for evaluating RAG performance and achieving hallucination reduction.

- Reliability assessment frameworks for LLM-RAG systems.

- Explainability techniques for understanding and interpreting RAG-based outputs.

- Standardizing protocols for reliable LLM and RAG evaluations.

- Hybrid evaluation frameworks combining automated and human annotations.

- User-centric evaluations, subjective assessments, and the personalization of RAG systems.

- Ethical considerations, bias mitigation, and transparency in combined LLM-RAG systems.

Human–Robot Interaction Applications:

- LLM-RAG systems for enhanced natural language understanding in human–robot interaction.

- Grounding robot actions through knowledge retrieval and contextual understanding.

- Vision–Language–Action Models (VLAs) that fuse vision, language, and actions.

- Safety mechanisms and reliability assessment for LLM-powered robotic systems.

- Multi-agent collaboration frameworks for robotic systems.

- Knowledge graph utilization for robotic task planning and execution.

Quantum Computing Integration:

- Quantum-enhanced information retrieval for RAG systems.

- Vector search optimization using quantum computing approaches.

- Theoretical frameworks and experimental implementations of quantum algorithms for document retrieval.

- Hybrid quantum–classical approaches for large-scale information retrieval.

- Performance comparisons between classical and quantum-enhanced RAG systems.

- Quantum code generation and assistance using LLMs and RAG.

- RAG frameworks for domain-specific quantum programming using frameworks like PennyLane and Qiskit.

Domain-Specific Applications:

- Domain-specific RAG applications in healthcare, legal and scientific research, and other specialized fields.

- Knowledge graph construction and utilization in specialized domains.

- Dataset curation and annotation methodologies for training LLMs in specialized domains.

- Explainable AI approaches for domain-specific applications requiring interpretability.

Dr. Ermelinda Oro
Guest Editor

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• large language models
• retrieval-augmented generation
• natural language processing
• knowledge graphs
• knowledge grounding
• human–robot interaction
• agentic computing
• vision–language–action models
• quantum computing
• quantum-enhanced information retrieval
• evaluation methodologies
• reliability assessment
• explainability
• hallucination mitigation
• vector search optimization
• quantum code generation
• multi-modal RAG