Large Language Models for Knowledge-Grounded Reasoning and Decision Support
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: 31 May 2027 | Viewed by 14
Editor
Interests: large language models; formal knowledge representation; automated reasoning; machine learning; semantic web
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The proposed Special Issue, “Large Language Models for Knowledge-Grounded Reasoning and Decision Support,” aims to provide a focused forum for high-quality research on the development and application of large language models (LLMs) in contexts where reliable reasoning, structured knowledge, and informed decision support are essential. The main focus is on methods, architectures, and applications that extend LLMs beyond general-purpose text generation towards knowledge-grounded, explainable, context-aware, and practically useful intelligent systems.
This Special Issue welcomes both theoretical and applied contributions. Relevant topics include, but are not limited to, retrieval-augmented generation, knowledge-graph-enhanced LLMs, ontology-based grounding, neuro-symbolic approaches, reasoning and planning with LLMs, tool use, agentic workflows, domain-specific adaptation, uncertainty handling, hallucination mitigation, interpretability, explainability, evaluation methodologies, and trustworthy deployment. Papers addressing decision support in specific domains, such as engineering, public administration, science, or industry, are also encouraged, provided they offer a clear methodological, technical, or empirical contribution.
The aim of this Special Issue is to consolidate and advance a rapidly developing research area at the intersection of natural language processing, machine learning, knowledge engineering, human–computer interaction, and decision-support systems. As LLMs are increasingly integrated into high-value and high-risk environments, there is a growing need for research examining how these models can be grounded in reliable knowledge sources, constrained by formal or semi-formal representations, and evaluated with respect to reasoning quality, factual accuracy, transparency, and decision usefulness.
In relation to the existing literature, this Special Issue will supplement current research by moving beyond isolated demonstrations of LLM capabilities and instead emphasizing their integration into knowledge-intensive and decision-oriented systems. In doing so, it seeks to support a more rigorous academic discussion on how LLMs can contribute to trustworthy, explainable, and evidence-based intelligent decision support.
Dr. Marko Horvat
Guest Editor
Manuscript Submission Information
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Keywords
- large language models
- generative artificial intelligence
- neuro-symbolic artificial intelligence
- knowledge graphs
- ontologies
- prompt engineering
- retrieval-augmented generation
- multimodal learning
- model explainability
- model alignment
- AI safety
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