Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 13 | |
| Section | Large Language Models, Generative AI, and Multimodal Learning | |
| DOI | https://doi.org/10.1051/itmconf/20268403005 | |
| Published online | 06 April 2026 | |
- K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl, et al. “Large language models encode clinical knowledge.” Nature, vol. 620, no. 7972, pp. 172–180, (2023) [CrossRef] [PubMed] [Google Scholar]
- Y. Li, Z. Li, K. Zhang, R. Dan, S. Jiang, and Y. Zhang. “ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge.” Cureus, vol. 15, no. 8, p. e44138, (2023) [Google Scholar]
- Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung. “Survey of Hallucination in Natural Language Generation.” ACM Computing Surveys, vol. 55, no. 12, pp. 1–38, (2023) [Google Scholar]
- World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization, (2021) [Google Scholar]
- D. Jin, E. Pan, N. Oufattole, W. H. Weng, H. Fang, and P. Szolovits. “What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams.” Journal of the American Medical Informatics Association, vol. 28, no. 11, pp. 2454–2461, (2021) [Google Scholar]
- A. Pal, D. Karkhanis, S. Dooley, M. Roberts, and S. Naidu. “FactCheck: Assessing the Factual Accuracy of Language Models in Real-World Scenarios.” In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics. (2024) [Google Scholar]
- P. Manakul, A. Liusie, and M. J. F. Gales. “SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.” In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 9004–9017. (2023) [Google Scholar]
- K. Guu, K. Lee, Z. Tung, P. Pasupat, and M. Chang. “Retrieval Augmented Language Model Pre-Training.” In Proceedings of the 37th International Conference on Machine Learning, vol. 119, pp. 3929–3938. PMLR, (2020) [Google Scholar]
- M. Yasunaga, J. Leskovec, and P. Liang. “LinkBERT: Pretraining Language Models with Document Links.” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 4003–4017. (2022) [Google Scholar]
- P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al. “Retrieval -Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems, vol. 33, pp. 9459–9474, (2020) [Google Scholar]
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