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While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized datasets, domain-specific tasks, or other private needs.
Deep reinforcement learning from human preferences
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Loss surfaces, mode connectivity, and fast ensembling of dnns
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Fine-tuning language models from human preferences
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Lm-debugger: An interactive tool for inspection and intervention in transformer-based language models
Mor Geva, Avi Caciularu, Guy Dar, Paul Roit, Shoval Sadde, Micah Shlain, Bar Tamir, and Yoav Goldberg · 2022
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Improving alignment of dialogue agents via targeted human judgements
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Lora: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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Patching open-vocabulary models by interpolating weights
Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, and Ludwig Schmidt · 2022
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Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A Smith, and Luke Zettlemoyer · 2022
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Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel · 2022
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Fast model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning · 2022
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Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn · 2022
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Fixing model bugs with natural language patches
Shikhar Murty, Christopher D Manning, Scott Lundberg, and Marco Tulio Ribeiro · 2022
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Training language models to follow instructions with human feedback
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Adaptive testing and debugging of nlp models
Marco Tulio Ribeiro and Scott Lundberg · 2022
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Extracting latent steering vectors from pretrained language models
Nishant Subramani, Nivedita Suresh, and Matthew E Peters · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Shadow alignment: The ease of subverting safely-aligned language models
Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, and Dahua Lin · 2023
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Low-resource languages jailbreak gpt-4
Zheng-Xin Yong, Cristina Menghini, and Stephen H Bach · 2023
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Rrhf: Rank responses to align language models with human feedback without tears
Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang · 2023
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Removing rlhf protections in gpt-4 via fine-tuning
Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, and Daniel Kang · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson · 2023
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Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
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Enhancing chat language models by scaling high-quality instructional conversations
Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou · 2023
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Koala: A dialogue model for academic research
Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song · 2023
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Editing models with task arithmetic
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Jailbroken: How does llm safety training fail?
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Judging llm-as-a-judge with mt-bench and chatbot arena
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