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Pretrained language models (LMs) encode implicit representations of knowledge in their parameters.
Wordnet: A lexical database for english
George A. Miller. 1995 · 1995
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013 · 2013
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Commonsense knowledge base completion
Xiang Li, Aynaz Taheri, Lifu Tu, and Kevin Gimpel. 2016 · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2017 · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. 2017 · 2017
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017 · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik. 2018 · 2018
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Analysis methods in neural language processing: A survey
Yonatan Belinkov and James Glass. 2019 · 2019
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COMET: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 2019
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What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin. 2019 · 2019
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KagNet: Knowledge-aware graph networks for commonsense reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, and Xiang Ren. 2019 · 2019
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Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019a · 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 models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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BERT rediscovers the classical NLP pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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How do decisions emerge across layers in neural models? interpretation with differentiable masking
Nicola De Cao, Michael Sejr Schlichtkrull, Wilker Aziz, and Ivan Titov. 2020 · 2020
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Analyzing individual neurons in pre-trained language models
Nadir Durrani, Hassan Sajjad, Fahim Dalvi, and Yonatan Belinkov. 2020 · 2020
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(comet-) atomic 2020: On symbolic and neural commonsense knowledge graphs
Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. 2021 · 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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Zoom in: An introduction to circuits
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 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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Movement pruning: Adaptive sparsity by fine-tuning
Victor Sanh, Thomas Wolf, and Alexander Rush. 2020 · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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BERTnesia: Investigating the capture and forgetting of knowledge in BERT
Jonas Wallat, Jaspreet Singh, and Avishek Anand. 2020 · 2020
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Masking as an efficient alternative to finetuning for pretrained language models
Mengjie Zhao, Tao Lin, Fei Mi, Martin Jaggi, and Hinrich Schütze. 2020 · 2020
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Low-complexity probing via finding subnetworks
Steven Cao, Victor Sanh, and Alexander Rush. 2021b · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel. 2021 · 2021
Discovering language-neutral sub-networks in multilingual language models
Negar Foroutan, Mohammadreza Banaei, Rémi Lebret, Antoine Bosselut, and Karl Aberer. 2022 · 2022
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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. 2022a · 2022
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Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space
Mor Geva, Avi Caciularu, Kevin Wang, and Yoav Goldberg. 2022b · 2022
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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 · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022 · 2022
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Are neural nets modular? inspecting functional modularity through differentiable weight masks
Róbert Csordás, Sjoerd van Steenkiste, and Jürgen Schmidhuber. 2021 · 2021
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Analyzing commonsense emergence in few-shot knowledge models
Jeff Da, Ronan Le Bras, Ximing Lu, Yejin Choi, and Antoine Bosselut. 2021 · 2021
Cited alongside, same era.
Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2021 · 2021
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Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
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Parameter-efficient transfer learning with diff pruning
Demi Guo, Alexander Rush, and Yoon Kim. 2021 · 2021
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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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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2022 · 2022
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Specializing pre-trained language models for better relational reasoning via network pruning
Siyu Ren and Kenny Zhu. 2022 · 2022
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2023 · 2023
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Reckoning: Reasoning through dynamic knowledge encoding
Zeming Chen, Gail Weiss, Eric Mitchell, Asli Celikyilmaz, and Antoine Bosselut. 2023 · 2023
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Identifying and adapting transformer-components responsible for gender bias in an English language model
Abhijith Chintam, Rahel Beloch, Willem Zuidema, Michael Hanna, and Oskar van der Wal. 2023 · 2023
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Towards automated circuit discovery for mechanistic interpretability
Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso. 2023 · 2023
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Dissecting recall of factual associations in auto-regressive language models
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023 · 2023
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Localizing model behavior with path patching
Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, and Aryaman Arora. 2023 · 2023
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Editing common sense in transformers
Anshita Gupta, Debanjan Mondal, Akshay Sheshadri, Wenlong Zhao, Xiang Li, Sarah Wiegreffe, and Niket Tandon. 2023 · 2023
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Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo. 2023 · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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Mass-editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex J Andonian, Yonatan Belinkov, and David Bau. 2023 · 2023
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Interpretability in the wild: a circuit for indirect object identification in GPT-2 small
Kevin Ro Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt. 2023 · 2023
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Generate rather than retrieve: Large language models are strong context generators
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ROME: Evaluating pre-trained vision-language models on reasoning beyond visual common sense
Kankan Zhou, Eason Lai, Wei Bin Au Yeong, Kyriakos Mouratidis, and Jing Jiang. 2023 · 2023
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Inspecting and editing knowledge representations in language models
Evan Hernandez, Belinda Z. Li, and Jacob Andreas. 2024 · 2024
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Circuit breaking: Removing model behaviors with targeted ablation
Maximilian Li, Xander Davies, and Max Nadeau. 2024 · 2024
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Large language models relearn removed concepts
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