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This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models.
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Think you have solved question answering? try arc, the ai2 reasoning challenge, 2018
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Preventing verbatim memorization in language models gives a false sense of privacy
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How our principles helped define alphafold’s release, 2022
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Training language models to follow instructions with human feedback
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Scaling up models and data with t5x
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Defining and characterizing reward gaming
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Commonsenseqa: A question answering challenge targeting commonsense knowledge, 2019
A. Talmor, J. Herzig, N. Lourie, and J. Berant · 2019
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Xla: Optimizing compiler for tensorflow, 2019
XLA · 2019
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Program synthesis with large language models
J. Austin, A. Odena, M. I. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. J. Cai, M. Terry, Q. V. Le, and C. Sutton · 2021
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Evaluating large language models trained on code
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Training verifiers to solve math word problems
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, C. Hesse, and J. Schulman · 2021
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Measuring mathematical problem solving with the math dataset
D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt · 2021
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Roformer: Enhanced transformer with rotary position embedding
J. Su, Y. Lu, S. Pan, B. Wen, and Y. Liu · 2021
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J. M. V. Skalse, N. H. R. Howe, D. Krasheninnikov, and D. Krueger · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them, 2022
M. Suzgun, N. Scales, N. Schärli, S. Gehrmann, Y. Tay, H. W. Chung, A. Chowdhery, Q. V. Le, E. H. Chi, D. Zhou, and J. Wei · 2022
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Chain of thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. H. Chi, Q. Le, and D. Zhou · 2022
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The falcon series of open language models, 2023
E. Almazrouei, H. Alobeidli, A. Alshamsi, A. Cappelli, R. Cojocaru, M. Debbah, Étienne Goffinet, D. Hesslow, J. Launay, Q. Malartic, D. Mazzotta, B. Noune, B. Pannier, and G. Penedo · 2023
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R. Anil, A. M. Dai, O. Firat, M. Johnson, D. Lepikhin, A. Passos, S. Shakeri, E. Taropa, P. Bailey, Z. Chen, et al · 2023
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Gemini: A family of highly capable multimodal models, 2023
Gemini Team · 2023
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Mistral 7b, 2023
A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. de las Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, L. R. Lavaud, M.-A. Lachaux, P. Stock, T. L. Scao, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed · 2023
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Madlad-400: A multilingual and document-level large audited dataset
S. Kudugunta, I. Caswell, B. Zhang, X. Garcia, C. A. Choquette-Choo, K. Lee, D. Xin, A. Kusupati, R. Stella, A. Bapna, et al · 2023
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Scalable extraction of training data from (production) language models
M. Nasr, N. Carlini, J. Hayase, M. Jagielski, A. F. Cooper, D. Ippolito, C. A. Choquette-Choo, E. Wallace, F. Tramèr, and K. Lee · 2023
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How to catch an ai liar: Lie detection in black-box llms by asking unrelated questions, 2023
L. Pacchiardi, A. J. Chan, S. Mindermann, I. Moscovitz, A. Y. Pan, Y. Gal, O. Evans, and J. Brauner · 2023
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Scaling up models and data with t5x and seqio
A. Roberts, H. W. Chung, G. Mishra, A. Levskaya, J. Bradbury, D. Andor, S. Narang, B. Lester, C. Gaffney, A. Mohiuddin, et al · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. P. Xing, H. Zhang, J. E. Gonzalez, and I. Stoica · 2023
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Agieval: A human-centric benchmark for evaluating foundation models, 2023
W. Zhong, R. Cui, Y. Guo, Y. Liang, S. Lu, Y. Wang, A. Saied, W. Chen, and N. Duan · 2023
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Representation engineering: A top-down approach to ai transparency, 2023
A. Zou, L. Phan, S. Chen, J. Campbell, P. Guo, R. Ren, A. Pan, X. Yin, M. Mazeika, A.-K. Dombrowski, S. Goel, N. Li, M. J. Byun, Z. Wang, A. Mallen, S. Basart, S. Koyejo, D. Song, M. Fredrikson, J. Z. Kolter, and D. Hendrycks · 2023
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