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We call into question the recently popularized method of direct model editing as a means of correcting factual errors in LLM generations.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Roger Ratcliff. 1990 · 1990
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Complexity classification of truth maintenance systems
Vladislav Rutenburg. 1991 · 1991
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Modifying memories in transformer models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, and Sanjiv Kumar. 2020 · 2012
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Hermit: An owl 2 reasoner
Birte Glimm, Ian Horrocks, Boris Motik, Giorgos Stoilos, and Zhe Wang. 2014 · 2014
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Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Bias and fairness in natural language processing
Kai-Wei Chang, Vinodkumar Prabhakaran, and Vicente Ordonez. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020 · 2020
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Optimal continual learning has perfect memory and is np-hard
Jeremias Knoblauch, Hisham Husain, and Tom Diethe. 2020 · 2020
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Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
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Editable neural networks
Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Pyrkin, Sergei Popov, and Artem Babenko. 2020 · 2020
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Facts as experts: Adaptable and interpretable neural memory over symbolic knowledge
Pat Verga, Haitian Sun, Livio Baldini Soares, and William W. Cohen. 2020 · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, T. W. Hennigan, Saffron Huang, Lorenzo Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, and L. Sifre. 2021 · 2021
Cited alongside, same era.
Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
Cited alongside, same era.
Towards continual knowledge learning of language models
Joel Jang, Seonghyeon Ye, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Stanley Jungkyu Choi, and Minjoon Seo. 2021 · 2021
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Leace: Perfect linear concept erasure in closed form
Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, and Stella Biderman. 2023 · 2023
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Robustness of edited neural networks
Davis Brown, Charles Godfrey, Cody Nizinski, Jonathan Tu, and Henry Kvinge. 2023 · 2023
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Grounding large language models in interactive environments with online reinforcement learning
Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. 2023 · 2023
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Evaluating the ripple effects of knowledge editing in language models
Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2023 · 2023
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Cook: Empowering general-purpose language models with modular and collaborative knowledge
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Do we know what we don’t know? studying unanswerable questions beyond squad 2.0
Elior Sulem, Jamaal Hay, and Dan Roth. 2021 · 2021
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A review on language models as knowledge bases
Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, Mona T. Diab, and Marjan Ghazvininejad. 2022 · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan. 2022 · 2022
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Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane A. Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022 · 2022
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Kamel: Knowledge analysis with multitoken entities in language models
Jan-Christoph Kalo and Leandra Fichtel. 2022 · 2022
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Realtime qa: What’s the answer right now?
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, and Kentaro Inui. 2022 · 2022
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Factuality enhanced language models for open-ended text generation
Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, and Bryan Catanzaro. 2022 · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
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Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, and Yulia Tsvetkov. 2023 · 2023
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Language model analysis for ontology subsumption inference
Yuan He, Jiaoyan Chen, Ernesto Jimenez-Ruiz, Hang Dong, and Ian Horrocks. 2023 · 2023
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Detecting edit failures in large language models: An improved specificity benchmark
Jason Hoelscher-Obermaier, Julia Persson, Esben Kran, Ioannis Konstas, and Fazl Barez. 2023 · 2023
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Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023 · 2023
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Sail: Search-augmented instruction learning
Hongyin Luo, Yung-Sung Chuang, Yuan Gong, Tianhua Zhang, Yoon Kim, Xixin Wu, Danny Fox, Helen Meng, and James Glass. 2023 · 2023
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Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
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Can lms learn new entities from descriptions? challenges in propagating injected knowledge
Yasumasa Onoe, Michael J. Q. Zhang, Shankar Padmanabhan, Greg Durrett, and Eunsol Choi. 2023 · 2023
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Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023 · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
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Is reinforcement learning (not) for natural language processing: Benchmarks, baselines, and building blocks for natural language policy optimization
Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, and Yejin Choi. 2023 · 2023
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Progressive prompts: Continual learning for language models
Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, and Amjad Almahairi. 2023 · 2023
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Kai Sun, Y. Xu, Hanwen Zha, Yue Liu, and Xinhsuai Dong. 2023 · 2023
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Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng, and Tat-seng Chua. 2023 · 2023
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