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Knowledge Editing (KE) algorithms alter models' weights to perform targeted updates to incorrect, outdated, or otherwise unwanted factual associations.
A simple neural network generating an interactive memory
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A global geometric framework for nonlinear dimensionality reduction
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Plato on knowledge in the theaetetus
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The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep
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Rewriting a deep generative model
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Transformer feed-forward layers are key-value memories
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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Knowledge neurons in pretrained transformers
Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., and Wei, F · 2021
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Editing factual knowledge in language models
De Cao, N., Aziz, W., and Titov, I · 2021
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NanoGPT , 2021
Karpathy, A · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
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Improving language models by retrieving from trillions of tokens
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Van Den Driessche, G. B., Lespiau, J.-B., Damoc, B., Clark, A., et al · 2022
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Data distributional properties drive emergent in-context learning in transformers
Chan, S., Santoro, A., Lampinen, A., Wang, J., Singh, A., Richemond, P., McClelland, J., and Hill, F · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2022
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Attractor and integrator networks in the brain
Khona, M. and Fiete, I. R · 2022
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Fast Model Editing at Scale, June 2022
Mitchell, E., Lin, C., Bosselut, A., Finn, C., and Manning, C. D · 2022
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Lamda: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4
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Editing Language Model-based Knowledge Graph Embeddings, December 2023
Cheng, S., Zhang, N., Tian, B., Chen, X., Liu, Q., and Chen, H · 2023
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NNsight and NDIF: Democratizing access to foundation model internals
Fiotto-Kaufman, J., Loftus, A. R., Todd, E., Brinkmann, J., Juang, C., Pal, K., Rager, C., Mueller, A., Marks, S., Sharma, A. S., Lucchetti, F., Ripa, M., Belfki, A., Prakash, N., Multani, S., Brodley, C., Guha, A., Bell, J., Wallace, B., and Bau, D · 2024
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Are we done with mmlu?, 2024
Gema, A. P., Leang, J. O. J., Hong, G., Devoto, A., Mancino, A. C. M., Saxena, R., He, X., Zhao, Y., Du, X., Madani, M. R. G., Barale, C., McHardy, R., Harris, J., Kaddour, J., van Krieken, E., and Minervini, P · 2024
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The llama 3 herd of models, 2024
Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., et al · 2024
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Model editing harms general abilities of large language models: Regularization to the rescue
Gu, J.-C., Xu, H.-X., Ma, J.-Y., Lu, P., Ling, Z.-H., Chang, K.-W., and Peng, N · 2024
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Cohen, R., Biran, E., Yoran, O., Globerson, A., and Geva, M · 2023
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al · 2023
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Towards revealing the mystery behind chain of thought: a theoretical perspective
Feng, G., Gu, Y., Zhang, B., Ye, H., He, D., and Wang, L · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T · 2023
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Hase, P., Bansal, M., Kim, B., and Ghandeharioun, A · 2023
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Superposition, memorization, and double descent
Henighan, T., Carter, S., Hume, T., Elhage, N., Lasenby, R., Fort, S., Schiefer, N., and Olah, C · 2023
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Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark, June 2023
Hoelscher-Obermaier, J., Persson, J., Kran, E., Konstas, I., and Barez, F · 2023
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Hase, P., Hofweber, T., Zhou, X., Stengel-Eskin, E., and Bansal, M · 2024
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Are language models rational? the case of coherence norms and belief revision
Hofweber, T., Hase, P., Stengel-Eskin, E., and Bansal, M · 2024
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Towards an understanding of stepwise inference in transformers: A synthetic graph navigation model
Khona, M., Okawa, M., Hula, J., Ramesh, R., Nishi, K., Dick, R., Lubana, E. S., and Tanaka, H · 2024
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Pmet: Precise model editing in a transformer
Li, X., Li, S., Song, S., Yang, J., Ma, J., and Yu, J · 2024
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ZeroEval: A Unified Framework for Evaluating Language Models, July 2024
Lin, B. Y · 2024
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A percolation model of emergence: Analyzing transformers trained on a formal language
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Eight methods to evaluate robust unlearning in llms
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Why think step by step? reasoning emerges from the locality of experience
Prystawski, B., Li, M., and Goodman, N · 2024
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Locating and editing factual associations in mamba
Sharma, A. S., Atkinson, D., and Bau, D · 2024
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Wise: Rethinking the knowledge memory for lifelong model editing of large language models
Wang, P., Li, Z., Zhang, N., Xu, Z., Yao, Y., Jiang, Y., Xie, P., Huang, F., and Chen, H · 2024
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Be Like Mike , 2024
Wikipedia · 2024
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Is bigger edit batch size always better?–an empirical study on model editing with llama-3
Yoon, J., Gupta, A., and Anumanchipalli, G · 2024
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Modifying llm beliefs with synthetic document finetuning
Wang, R., Griffin, A., Treutlein, J., Perez, E., Michael, J., Roger, F., and Marks, S · 2025
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What algorithms can transformers learn? a study in length generalization
Zhou, H., Bradley, A., Littwin, E., Razin, N., Saremi, O., Susskind, J., Bengio, S., and Nakkiran, P · 2025
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