Large Language Models: A Structured Taxonomy and Review of Challenges, Limitations, Solutions, and Future Directions
Large language models (LLMs), as one of the most advanced achievements in the field of natural language processing (NLP), have made significant progress in areas such as natural language understanding and generation. However, attempts to achieve the widespread use of these models have met numerous c...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/8103 |
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| Summary: | Large language models (LLMs), as one of the most advanced achievements in the field of natural language processing (NLP), have made significant progress in areas such as natural language understanding and generation. However, attempts to achieve the widespread use of these models have met numerous challenges, encompassing technical, social, ethical, and legal aspects. This paper provides a comprehensive review of the various challenges associated with LLMs and analyzes the key issues related to these technologies. Among the challenges discussed are model interpretability, biases in data and model outcomes, ethical concerns regarding privacy and data security, and their high computational requirements. Furthermore, the paper examines how these challenges impact the applications of LLMs in fields such as healthcare, law, media, and education, emphasizing the importance of addressing these issues in the development and deployment of these models. Additionally, solutions for improving the robustness and control of models against biases and quality issues are proposed. Finally, the paper looks at the future of LLM research and the challenges that need to be addressed for the responsible and effective use of this technology. The goal of this paper is to provide a comprehensive analysis of the challenges and issues surrounding LLMs in order to enable the optimal and ethical use of these technologies in real-world applications. |
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| ISSN: | 2076-3417 |