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논문리뷰6

[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.
[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.
[논문리뷰] Fine-Tuning or Retrieval? Comparing Knowledge Injections in LLMs Ovadia, O., Brief, M., Mishaeli, M., & Elisha, O. (2023). Fine-tuning or retrieval? comparing knowledge injection in llms. arXiv preprint arXiv:2312.05934.https://arxiv.org/abs/2312.05934 Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMsLarge language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to an.. 2024. 10. 15.
[논문리뷰] LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-tuning of Large Language Models Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, and Roy Lee. 2023. LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5254–5276, Singapore. Association for Computational Linguistics. https://arxiv.org/abs/2304... 2024. 10. 14.
[논문리뷰] What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? What Language Model Architecture and Pretraining Objectvie Work Best for Zero-Shot Generalization, International Conference on Machine Learning, PMLR (Proceedings of Machine Learning Research), 2022.https://arxiv.org/abs/2204.05832 What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?Large pretrained Transformer language models have been shown to exhi.. 2024. 9. 30.
[논문리뷰] LIMA: Less Is More for Alignment LIMA: Less Is More for Alignment, In Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS '23).  Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy.https://arxiv.org/abs/2305.11206 LIMA: Less Is More for AlignmentLarge lang.. 2024. 9. 23.
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