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Decoder-based transformers, while revolutionizing language modeling and scaling to immense sizes, have not completely overtaken encoder-heavy architectures in natural language processing.
Superglue: A stickier benchmark for general-purpose language understanding systems, 2020
A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman · 1905
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Learning to rank using gradient descent
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender · 2005
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Learning to Rank for Information Retrieval
T.-Y. Liu · 2011
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Ms marco: A human generated machine reading comprehension dataset, 2018
P. Bajaj, D. Campos, N. Craswell, L. Deng, J. Gao, X. Liu, R. Majumder, A. McNamara, B. Mitra, T. Nguyen, M. Rosenberg, X. Song, A. Stoica, S. Tiwary, and T. Wang · 2018
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An analysis of the softmax cross entropy loss for learning-to-rank with binary relevance
S. Bruch, X. Wang, M. Bendersky, and M. Najork · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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Glue: A multi-task benchmark and analysis platform for natural language understanding, 2019
A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman · 2019
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A stochastic treatment of learning to rank scoring functions
S. Bruch, S. Han, M. Bendersky, and M. Najork · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020
Cited alongside, same era.
Palm: Scaling language modeling with pathways, 2022
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, P. Schuh, K. Shi, S. Tsvyashchenko, J. Maynez, A. Rao, P. Barnes, Y. Tay, N. Shazeer, V. Prabhakaran, E. Reif, N. Du, B. Hutchinson, R. Pope, J. Bradbury, J. Austin, M. Isard, G. Gur-Ari, P. Yin, T. Duke, A. Levskaya, S. Ghemawat, S. Dev, H. Michalewski, X. Garcia, V. Misra, K. Robinson, L. Fedus, D. Zhou, D. Ippolito, D. Luan, H. Lim, B. Zoph, A. Spiridonov, R. Sepassi, D. Dohan, S. Agrawal, M. Omernick, A. M. Dai, T. S. Pillai, M. Pellat, A. Lewkowycz, E. Moreira, R. Child, O. Polozov, K. Lee, Z. Zhou, X. Wang, B. Saeta, M. Diaz, O. Firat, M. Catasta, J. Wei, K. Meier-Hellstern, D. Eck, J. Dean, S. Petrov, and N. Fiedel · 2022
Cited alongside, same era.
Exploring dual encoder architectures for question answering, 2022
Z. Dong, J. Ni, D. M. Bikel, E. Alfonseca, Y. Wang, C. Qu, and I. Zitouni · 2022
Rankt5: Fine-tuning t5 for text ranking with ranking losses, 2022
H. Zhuang, Z. Qin, R. Jagerman, K. Hui, J. Ma, J. Lu, J. Ni, X. Wang, and M. Bendersky · 2022
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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, E. Chu, J. H. Clark, L. E. Shafey, Y. Huang, K. Meier-Hellstern, G. Mishra, E. Moreira, M. Omernick, K. Robinson, S. Ruder, Y. Tay, K. Xiao, Y. Xu, Y. Zhang, G. H. Abrego, J. Ahn, J. Austin, P. Barham, J. Botha, J. Bradbury, S. Brahma, K. Brooks, M. Catasta, Y. Cheng, C. Cherry, C. A. Choquette-Choo, A. Chowdhery, C. Crepy, S. Dave, M. Dehghani, S. Dev, J. Devlin, M. Díaz, N. Du, E. Dyer, V. Feinberg, F. Feng, V. Fienber, M. Freitag, X. Garcia, S. Gehrmann, L. Gonzalez, G. Gur-Ari, S. Hand, H. Hashemi, L. Hou, J. Howland, A. Hu, J. Hui, J. Hurwitz, M. Isard, A. Ittycheriah, M. Jagielski, W. Jia, K. Kenealy, M. Krikun, S. Kudugunta, C. Lan, K. Lee, B. Lee, E. Li, M. Li, W. Li, Y. Li, J. Li, H. Lim, H. Lin, Z. Liu, F. Liu, M. Maggioni, A. Mahendru, J. Maynez, V. Misra, M. Moussalem, Z. Nado, J. Nham, E. Ni, A. Nystrom, A. Parrish, M. Pellat, M. Polacek, A. Polozov, R. Pope, S. Qiao, E. Reif, B. Richter, P. Riley, A. C. Ros, A. Roy, B. Saeta, R. Samuel, R. Shelby, A. Slone, D. Smilkov, D. R. So, D. Sohn, S. Tokumine, D. Valter, V. Vasudevan, K. Vodrahalli, X. Wang, P. Wang, Z. Wang, T. Wang, J. Wieting, Y. Wu, K. Xu, Y. Xu, L. Xue, P. Yin, J. Yu, Q. Zhang, S. Zheng, C. Zheng, W. Zhou, D. Zhou, S. Petrov, and Y. Wu · 2023
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Training compute-optimal large language models, 2022
J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de Las Casas, L. A. Hendricks, J. Welbl, A. Clark, T. Hennigan, E. Noland, K. Millican, G. van den Driessche, B. Damoc, A. Guy, S. Osindero, K. Simonyan, E. Elsen, J. W. Rae, O. Vinyals, and L. Sifre · 2022
Cited alongside, same era.
Perceiver IO: A general architecture for structured inputs & outputs
A. Jaegle, S. Borgeaud, J.-B. Alayrac, C. Doersch, C. Ionescu, D. Ding, S. Koppula, D. Zoran, A. Brock, E. Shelhamer, O. J. Henaff, M. Botvinick, A. Zisserman, O. Vinyals, and J. Carreira · 2022
Cited alongside, same era.
Polyloss: A polynomial expansion perspective of classification loss functions
Z. Leng, M. Tan, C. Liu, E. D. Cubuk, J. Shi, S. Cheng, and D. Anguelov · 2022
Cited alongside, same era.
Gemma 2: Improving open language models at a practical size, 2024a
Gemma Team
Cited in the paper.
Gemma: Open models based on gemini research and technology, 2024b
Gemma Team
Cited in the paper.
Gemini Team · 2023
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F. Moiseev, G. H. Abrego, P. Dornbach, I. Zitouni, E. Alfonseca, and Z. Dong · 2023
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OpenAI · 2023
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Gecko: Versatile text embeddings distilled from large language models, 2024
J. Lee, Z. Dai, X. Ren, B. Chen, D. Cer, J. R. Cole, K. Hui, M. Boratko, R. Kapadia, W. Ding, Y. Luan, S. M. K. Duddu, G. H. Abrego, W. Shi, N. Gupta, A. Kusupati, P. Jain, S. R. Jonnalagadda, M.-W. Chang, and I. Naim · 2024
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