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The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources.
Large language models in machine translation
Thorsten Brants, Ashok C. Popat, Peng Xu, Franz J. Och, and Jeffrey Dean. 2007 · 2007
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Top languages in global information production
Sergey Lobachev. 2008 · 2008
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Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le. 2017 · 2017
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FEVER: a large-scale dataset for fact extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Unsupervised cross-lingual representation learning at scale
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Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Pyrkin, Sergei Popov, and Artem Babenko. 2020 · 2020
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CCNet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave. 2020 · 2020
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
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Do language models have beliefs? methods for detecting, updating, and visualizing model beliefs
Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, and Srinivasan Iyer. 2021 · 2021
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Editing a classifier by rewriting its prediction rules
Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, and Aleksander Madry. 2021 · 2021
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Towards tracing knowledge in language models back to the training data
Ekin Akyurek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, and Kelvin Guu. 2022 · 2022
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Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022 · 2022
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Calibrating factual knowledge in pretrained language models
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. 2022 · 2022
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Aging with grace: Lifelong model editing with discrete key-value adaptors
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2022 · 2022
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Fixing model bugs with natural language patches
Shikhar Murty, Christopher Manning, Scott Lundberg, and Marco Tulio Ribeiro. 2022 · 2022
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Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
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Repairing neural networks by leaving the right past behind
Ryutaro Tanno, Melanie F Pradier, Aditya Nori, and Yingzhen Li. 2022 · 2022
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Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun. 2023 · 2023
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Transformer-patcher: One mistake worth one neuron
Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2023 · 2023
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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 · 2022
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FRUIT: Faithfully reflecting updated information in text
Robert Iv, Alexandre Passos, Sameer Singh, and Ming-Wei Chang. 2022 · 2022
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The bigscience roots corpus: A 1.6 tb composite multilingual dataset
Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro Von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, et al. 2022 · 2022
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Plug-and-play adaptation for continuously-updated QA
Kyungjae Lee, Wookje Han, Seung-won Hwang, Hwaran Lee, Joonsuk Park, and Sang-Woo Lee. 2022 · 2022
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Large language models with controllable working memory
Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, and Sanjiv Kumar. 2022 · 2022
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Memory-assisted prompt editing to improve GPT-3 after deployment
Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang. 2022 · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022a
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Cross-lingual consistency of factual knowledge in multilingual language models
Jirui Qi, Raquel Fernández, and Arianna Bisazza. 2023 · 2023
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Prompting gpt-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Language anisotropic cross-lingual model editing
Yang Xu, Yutai Hou, Wanxiang Che, and Min Zhang. 2023 · 2023
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Editing large language models: Problems, methods, and opportunities
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023 · 2023
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Entailer: Answering questions with faithful and truthful chains of reasoning
Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark. 2022 · 2093
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