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Recent large language models often answer factual questions correctly.
Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
N. Jaques, A. Ghandeharioun, J. H. Shen, C. Ferguson, À. Lapedriza, N. Jones, S. Gu, and R. W. Picard · 1907
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CTRL: A conditional transformer language model for controllable generation
N. S. Keskar, B. McCann, L. R. Varshney, C. Xiong, and R. Socher · 1909
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Megatron-lm: Training multi-billion parameter language models using model parallelism
M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catanzaro · 1909
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Fine-tuning language models from human preferences
D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. F. Christiano, and G. Irving · 1909
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REALM: retrieval-augmented language model pre-training
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M. Chang · 2002
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Dense passage retrieval for open-domain question answering
V. Karpukhin, B. Oguz, S. Min, L. Wu, S. Edunov, D. Chen, and W. Yih · 2004
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Explaining question answering models through text generation
V. Latcinnik and J. Berant · 2004
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Wt5?! training text-to-text models to explain their predictions
S. Narang, C. Raffel, K. Lee, A. Roberts, N. Fiedel, and K. Malkan · 2004
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Explainable deep learning: A field guide for the uninitiated
G. Ras, N. Xie, M. van Gerven, and D. Doran · 2004
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Selective question answering under domain shift
A. Kamath, R. Jia, and P. Liang · 2006
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Leveraging passage retrieval with generative models for open domain question answering
G. Izacard and E. Grave · 2007
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QED: A framework and dataset for explanations in question answering
M. Lamm, J. Palomaki, C. Alberti, D. Andor, E. Choi, L. B. Soares, and M. Collins · 2009
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Learning to summarize from human feedback
N. Stiennon, L. Ouyang, J. Wu, D. M. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. F. Christiano · 2009
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On the foundations of noise-free selective classification
R. El-Yaniv et al · 2010
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Preference-based policy learning
R. Akrour, M. Schoenauer, and M. Sebag · 2011
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Programming by feedback
M. Schoenauer, R. Akrour, M. Sebag, and J.-C. Souplet · 2014
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Truth is a lie: Crowd truth and the seven myths of human annotation
L. Aroyo and C. Welty · 2015
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Deep learning with limited numerical precision
S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan · 2015
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang · 2016
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Model-free preference-based reinforcement learning
C. Wirth, J. Fürnkranz, and G. Neumann · 2016
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Deep reinforcement learning from human preferences, 2017
P. Christiano, J. Leike, T. B. Brown, M. Martic, S. Legg, and D. Amodei · 2017
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Selective classification for deep neural networks
Y. Geifman and R. El-Yaniv · 2017
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Bias in wikipedia
C. Hube · 2017
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Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
N. Jaques, S. Gu, D. Bahdanau, J. M. Hernández-Lobato, R. E. Turner, and D. Eck · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
M. Joshi, E. Choi, D. S. Weld, and L. Zettlemoyer · 2017
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How much knowledge can you pack into the parameters of a language model?
A. Roberts, C. Raffel, and N. Shazeer · 2020
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A general language assistant as a laboratory for alignment
A. Askell, Y. Bai, A. Chen, D. Drain, D. Ganguli, T. Henighan, A. Jones, N. Joseph, B. Mann, N. DasSarma, N. Elhage, Z. Hatfield-Dodds, D. Hernandez, J. Kernion, K. Ndousse, C. Olsson, D. Amodei, T. B. Brown, J. Clark, S. McCandlish, C. Olah, and J. Kaplan · 2021
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Improving language models by retrieving from trillions of tokens
S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. van den Driessche, J. Lespiau, B. Damoc, A. Clark, D. de Las Casas, A. Guy, J. Menick, R. Ring, T. Hennigan, S. Huang, L. Maggiore, C. Jones, A. Cassirer, A. Brock, M. Paganini, G. Irving, O. Vinyals, S. Osindero, K. Simonyan, J. W. Rae, E. Elsen, and L. Sifre · 2021
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Unitedqa: A hybrid approach for open domain question answering
H. Cheng, Y. Shen, X. Liu, P. He, W. Chen, and J. Gao · 2021
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Wirth, R. Akrour, G. Neumann, and J. Fürnkranz · 2017
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Supervising strong learners by amplifying weak experts
P. F. Christiano, B. Shlegeris, and D. Amodei · 2018
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G. Irving, P. Christiano, and D. Amodei · 2018
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Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
T. Kudo and J. Richardson · 2018
Cited alongside, same era.
Scalable agent alignment via reward modeling: a research direction
J. Leike, D. Krueger, T. Everitt, M. Martic, V. Maini, and S. Legg · 2018
Cited alongside, same era.
Persistent bias on wikipedia: Methods and responses
B. Martin · 2018
Cited alongside, same era.
Kickstarting deep reinforcement learning
S. Schmitt, J. J. Hudson, A. Zídek, S. Osindero, C. Doersch, W. M. Czarnecki, J. Z. Leibo, H. Küttler, A. Zisserman, K. Simonyan, and S. M. A. Eslami · 2018
Cited alongside, same era.
Training verifiers to solve math word problems
K. Cobbe, V. Kosaraju, M. Bavarian, J. Hilton, R. Nakano, C. Hesse, and J. Schulman · 2021
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Internet-augmented dialogue generation
M. Komeili, K. Shuster, and J. Weston · 2021
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Jurassic-1: Technical details and evaluation
O. Lieber, O. Sharir, B. Lenz, and Y. Shoham · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
S. Lin, J. Hilton, and O. Evans · 2021
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Webgpt: Browser-assisted question-answering with human feedback
R. Nakano, J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain, V. Kosaraju, W. Saunders, X. Jiang, K. Cobbe, T. Eloundou, G. Krueger, K. Button, M. Knight, B. Chess, and J. Schulman · 2021
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Quality: Question answering with long input texts, yes!
R. Y. Pang, A. Parrish, N. Joshi, N. Nangia, J. Phang, A. Chen, V. Padmakumar, J. Ma, J. Thompson, H. He, and S. R. Bowman · 2021
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Scaling language models: Methods, analysis & insights from training gopher
J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, H. F. Song, J. Aslanides, S. Henderson, R. Ring, S. Young, E. Rutherford, T. Hennigan, J. Menick, A. Cassirer, R. Powell, G. van den Driessche, L. A. Hendricks, M. Rauh, P. Huang, A. Glaese, J. Welbl, S. Dathathri, S. Huang, J. Uesato, J. Mellor, I. Higgins, A. Creswell, N. McAleese, A. Wu, E. Elsen, S. M. Jayakumar, E. Buchatskaya, D. Budden, E. Sutherland, K. Simonyan, M. Paganini, L. Sifre, L. Martens, X. L. Li, A. Kuncoro, A. Nematzadeh, E. Gribovskaya, D. Donato, A. Lazaridou, A. Mensch, J. Lespiau, M. Tsimpoukelli, N. Grigorev, D. Fritz, T. Sottiaux, M. Pajarskas, T. Pohlen, Z. Gong, D. Toyama, C. de Masson d’Autume, Y. Li, T. Terzi, V. Mikulik, I. Babuschkin, A. Clark, D. de Las Casas, A. Guy, C. Jones, J. Bradbury, M. Johnson, B. A. Hechtman, L. Weidinger, I. Gabriel, W. S. Isaac, E. Lockhart, S. Osindero, L. Rimell, C. Dyer, O. Vinyals, K. Ayoub, J. Stanway, L. Bennett, D. Hassabis, K. Kavukcuoglu, and G. Irving · 2021
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Ethical and social risks of harm from language models
L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, P.-S. Huang, M. Cheng, M. Glaese, B. Balle, A. Kasirzadeh, et al · 2021
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W. Zeng, X. Ren, T. Su, H. Wang, Y. Liao, Z. Wang, X. Jiang, Z. Yang, K. Wang, X. Zhang, C. Li, Z. Gong, Y. Yao, X. Huang, J. Wang, J. Yu, Q. Guo, Y. Yu, Y. Zhang, J. Wang, H. Tao, D. Yan, Z. Yi, F. Peng, F. Jiang, H. Zhang, L. Deng, Y. Zhang, Z. Lin, C. Zhang, S. Zhang, M. Guo, S. Gu, G. Fan, Y. Wang, X. Jin, Q. Liu, and Y. Tian · 2021
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Openai api
G. Brockman, M. Murati, P. Welinder, and OpenAI · 2022
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Cohere api | cohere
Cohere · 2022
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Internet-augmented language models through few-shot prompting for open-domain question answering
A. Lazaridou, E. Gribovskaya, W. Stokowiec, and N. Grigorev · 2022
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Streamingqa: A benchmark for adaptation to new knowledge over time in question answering models
A. Liska, T. Kocisky, E. Gribovskaya, T. Terzi, E. Sezener, D. Agrawal, C. de Masson d’Autume, T. Scholtes, M. Zaheer, S. Young, E. Gilsenan-McMahon, S. Austin, and A. Lazaridou · 2022
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WANLI: worker and AI collaboration for natural language inference dataset creation
A. Liu, S. Swayamdipta, N. A. Smith, and Y. Choi · 2022
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Red teaming language models with language models
E. Perez, S. Huang, H. F. Song, T. Cai, R. Ring, J. Aslanides, A. Glaese, N. McAleese, and G. Irving · 2022
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S. Smith, M. Patwary, B. Norick, P. LeGresley, S. Rajbhandari, J. Casper, Z. Liu, S. Prabhumoye, G. Zerveas, V. Korthikanti, E. Zheng, R. Child, R. Y. Aminabadi, J. Bernauer, X. Song, M. Shoeybi, Y. He, M. Houston, S. Tiwary, and B. Catanzaro · 2022
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Lamda: Language models for dialog applications, 2022
R. Thoppilan, D. D. Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, Y. Li, H. Lee, H. S. Zheng, A. Ghafouri, M. Menegali, Y. Huang, M. Krikun, D. Lepikhin, J. Qin, D. Chen, Y. Xu, Z. Chen, A. Roberts, M. Bosma, V. Zhao, Y. Zhou, C.-C. Chang, I. Krivokon, W. Rusch, M. Pickett, P. Srinivasan, L. Man, K. Meier-Hellstern, M. R. Morris, T. Doshi, R. D. Santos, T. Duke, J. Soraker, B. Zevenbergen, V. Prabhakaran, M. Diaz, B. Hutchinson, K. Olson, A. Molina, E. Hoffman-John, J. Lee, L. Aroyo, R. Rajakumar, A. Butryna, M. Lamm, V. Kuzmina, J. Fenton, A. Cohen, R. Bernstein, R. Kurzweil, B. Aguera-Arcas, C. Cui, M. Croak, E. Chi, and Q. Le · 2022
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