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Paper Review15

[논문리뷰] White-box Multimodal Jailbreaks Against Large Vision-Language Models Wang, R., Ma, X., Zhou, H., Ji, C., Ye, G., & Jiang, Y. (2024). White-box Multimodal Jailbreaks Against Large Vision-Language Models. ACM Multimedia.https://arxiv.org/abs/2405.17894 White-box Multimodal Jailbreaks Against Large Vision-Language ModelsRecent advancements in Large Vision-Language Models (VLMs) have underscored their superiority in various multimodal tasks. However, the adversarial .. 2024. 12. 27.
[논문리뷰] Visual Adversarial Examples Jailbreak Aligned Large Language Models Qi, X., Huang, K., Panda, A., Henderson, P., Wang, M., & Mittal, P. (2024). Visual Adversarial Examples Jailbreak Aligned Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence, 38(19), 21527-21536. https://doi.org/10.1609/aaai.v38i19.30150 https://arxiv.org/abs/2306.13213 Visual Adversarial Examples Jailbreak Aligned Large Language ModelsRecently, there has been a .. 2024. 12. 26.
[논문리뷰] Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization Zhexin Zhang, Junxiao Yang, Pei Ke, Fei Mi, Hongning Wang, and Minlie Huang. 2024. Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8865–8887, Bangkok, Thailand. Association for Computational Linguistics. https://aclanthology.org/2024... 2024. 12. 24.
[논문리뷰] "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models Shen, X., Chen, Z., Backes, M., Shen, Y., & Zhang, Y. (2023). " do anything now": Characterizing and evaluating in-the-wild jailbreak prompts on large language models. arXiv preprint arXiv:2308.03825.https://arxiv.org/abs/2308.03825 "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language ModelsThe misuse of large language models (LLMs) has drawn significa.. 2024. 12. 23.
Meta: Adapting Open Source Language Models 이번 블로그 포스트는 논문 리뷰는 아니고, Meta에서 운영하는 블로그 글의 리뷰입니다.LLaMa를 개발한 Meta가 어떻게 Open Source Large Language Models (LLMs)를 활용할 수 있을지,Part1: Methods for adapting large language models,Part2: To fine-tune or not to fine-tune,Part3: How to fine-tune: Focus on effective datasts로 나눠 설명하고 있는데, 이 내용을 좀 간추려보려 해요.논문에 비해서 훨씬 읽기 쉬운 글이니, 처음 LLM 모델을 접할 때 읽으면 좋을 것 같습니다! 먼저 각 part에 대해 요약해서 말하자면, part1에서는 LLM 모델의 활용을 개괄적으.. 2024. 10. 24.
[논문 리뷰] RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture Gupta, A., Shirgaonkar, A., Balaguer, A. D. L., Silva, B., Holstein, D., Li, D., ... & Benara, V. (2024). RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture. arXiv preprint arXiv:2401.08406.https://arxiv.org/abs/2401.08406 RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on AgricultureThere are two common ways in which developers are incorporating proprietary and.. 2024. 10. 16.
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