Open Access
Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
|
|
---|---|---|
Article Number | 02003 | |
Number of page(s) | 7 | |
Section | Machine Learning in Healthcare and Finance | |
DOI | https://doi.org/10.1051/itmconf/20257002003 | |
Published online | 23 January 2025 |
- I. J. Goodfellow, J. Shlens, and C. Szegedy. “Explaining and harnessing adversarial examples.” arXiv preprint arXiv:1412.6572 (2014). [Google Scholar]
- S. Kurt, et al. “Retrieval augmentation reduces hallucination in conversation.” arXiv preprint arXiv:2104.07567 (2021). [Google Scholar]
- C. Nicholas, et al. “On evaluating adversarial robustness.” arXiv preprint arXiv:1902.06705 (2019). [Google Scholar]
- R. Vipula, A. Sheth, and A. Das. “A survey of hallucination in large foundation models.” arXiv preprint arXiv:2309.05922 (2023). [Google Scholar]
- J. Y. Wang, et al. “Evaluation and analysis of hallucination in large vision-language models.” arXiv preprint arXiv:2308.15126 (2023). [Google Scholar]
- S. M. Tonmoy, et al. “A comprehensive survey of hallucination mitigation techniques in large language models.” arXiv preprint arXiv:2401.01313 (2024). [Google Scholar]
- X. Han, et al. “Adversarial attacks and defenses in images, graphs and text: A review.” International journal of automation and computing 17: 151-178. (2020). [CrossRef] [Google Scholar]
- A. Naveed and A. Mian. “Threat of adversarial attacks on deep learning in computer vision: A survey.” Ieee Access 6: 14410-14430 (2018). [Google Scholar]
- M. Aleksander, et al. “Towards deep learning models resistant to adversarial attacks.” arXiv preprint arXiv:1706.06083 (2017). [Google Scholar]
- X. D. Yu, et al. ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks. Findings of the Association for Computational Linguistics: NAACL 2024. (2024). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.