Neural Network Technologies in Natural Language Processing and Data Mining
Source abstract
Abstract
The rise of large language models (LLMs) has marked a significant leap in natural language processing (NLP) capabilities, enabling machines to understand, generate, and interact with human language at a sophisticated level [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Topic Modeling · Machine Learning in Healthcare · Big Data and Digital Economy
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, Croatia
Centre for Artificial Intelligence and Cybersecurity, University of Rijeka, 51000 Rijeka, Croatia
References (14)
Show source-provided references
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8. A Survey on Multimodal Large Language Models10.1093/nsr/nwae403 ↗
9. Navigating LLM Ethics: Advancements, Challenges, and Future Directions10.1007/s43681-025-00814-5 ↗
10. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., and Rocktäschel, T. (2021). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv.
11. Parthasarathy, V., Zafar, A., Khan, A., and Shahid, A. (2024). The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities. arXiv.
12. Nitu, M., and Dascalu, M. (2024). Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian. Appl. Sci., 14.10.3390/app14072700 ↗
13. James, A., Trovati, M., and Bolton, S. (2025). Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers. Appl. Sci., 15.10.3390/app15116247 ↗
14. Singh, A., Ehtesham, A., Kumar, S., and Khoei, T.T. (2025). Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG. arXiv.