Large Language Models and Their Limitations

Dear Colleagues,

Large Language Models (LLMs) have transformed the field of artificial intelligence, enabling applications ranging from natural language understanding to content generation. Despite their impressive capabilities, these models have notable deficiencies, including bias propagation, hallucinations, ethical challenges, and limitations in reasoning or domain-specific knowledge. This Special Issue aims to explore both the advancements and the shortcomings of LLMs, providing a comprehensive overview of current research and practical applications. Contributions may include original research articles, reviews, communications, and concept papers that address theoretical developments, evaluation methodologies, practical implementations, and strategies for mitigating model limitations. By bringing together researchers and practitioners, this issue will offer insights into improving model reliability, transparency, and ethical deployment. We encourage submissions that critically assess existing models, propose novel architectures, or explore interdisciplinary approaches to overcoming the inherent challenges of LLMs. The goal is to inform the broader AI community and provide a reference for future research in large-scale language modeling.

(1) Introduction, including scientific background and highlighting the importance of this research area.

Large Language Models (LLMs) have emerged as a transformative technology in artificial intelligence, driven by advances in deep learning, transformer architectures, and the availability of large-scale datasets. These models have achieved remarkable success in a wide range of applications, including natural language understanding, text generation, question answering, machine translation, sentiment analysis, and decision support systems. Their rapid integration into academic research, industry products, healthcare, education, and public services demonstrates their significant impact on modern data-driven and cognitive computing systems.

Despite these achievements, LLMs exhibit critical deficiencies that limit their reliability and responsible deployment. Common challenges include hallucinations, bias amplification, lack of transparency and explainability, limited reasoning and factual consistency, privacy and security risks, and high computational and environmental costs. These limitations raise fundamental scientific, ethical, and societal concerns, particularly in high-stakes domains. Addressing these issues is essential for advancing trustworthy AI, improving model robustness, and strengthening the theoretical foundations of large-scale language modeling. Consequently, research on the deficiencies of LLMs has become a crucial and timely topic within the broader AI and big data research community.

(2) Aim of the Special Issue and how the subject relates to the journal scope.

The aim of this Special Issue is to provide a dedicated platform for exploring the limitations, risks, and deficiencies of Large Language Models, while also highlighting emerging methods and frameworks to address these challenges. This Special Issue seeks to bring together researchers and practitioners to critically examine model behavior, evaluation strategies, mitigation techniques, and real-world implications of LLM deployment.

This topic strongly aligns with the scope of Big Data and Cognitive Computing (BDCC), which focuses on data-intensive intelligent systems, cognitive computing, scalable machine learning, and responsible AI technologies. LLMs fundamentally rely on big data and cognitive architectures, making BDCC an ideal venue for disseminating research that advances understanding of both their capabilities and shortcomings. Contributions to this Special Issue will support the development of more reliable, transparent, and ethically grounded language models and cognitive systems.

(3) Suggest themes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Bias, fairness, and ethical challenges in Large Language Models.
• Hallucination detection, analysis, and mitigation strategies.
• Explainability, interpretability, and transparency of LLMs.
• Evaluation benchmarks and reliability assessment of language models.
• Reasoning limitations and factual consistency in LLM-generated content.
• Privacy, security, and data leakage risks in large-scale models.
• Domain adaptation and low-resource challenges in LLMs.
• Energy efficiency, scalability, and sustainability of LLM training and deployment.
• Human–AI interaction, trust, and user perception of LLM-based systems.
• Real-world applications and case studies highlighting model deficiencies.

We look forward to receiving your contributions. 

Dr. Samira Zad
Guest Editor

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-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.

• large language models
• natural language processing
• big data analytics
• cognitive computing
• model bias
• hallucination
• explainable AI
• ethical AI