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Large language models (LLMs) face a daunting challenge due to the excessive computational and memory requirements of the commonly used Transformer architecture.
Gaussian error linear units (gelus)
Hendrycks, D. and Gimpel, K · 2016
Earlier work this paper cites.
Pointer sentinel mixture models, 2016
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016
Earlier work this paper cites.
The lambada dataset, Aug 2016
Paperno, D., Kruszewski, G., Lazaridou, A., Pham, Q. N., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., and Fernández, R · 2016
Earlier work this paper cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Crowdsourcing multiple choice science questions
Welbl, J., Liu, N. F., and Gardner, M · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
Earlier work this paper cites.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Elfwing, S., Uchibe, E., and Doya, K · 2018
Earlier work this paper cites.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language, 2019
Bisk, Y., Zellers, R., Bras, R. L., Gao, J., and Choi, Y · 2019
Earlier work this paper cites.
Boolq: Exploring the surprising difficulty of natural yes/no questions, 2019
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Decoupled weight decay regularization, 2019
Loshchilov, I. and Hutter, F · 2019
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Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K., Bras, R. L., Bhagavatula, C., and Choi, Y · 2019
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Hellaswag: Can a machine really finish your sentence?, 2019
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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Language models are few-shot learners, 2020
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Earlier work this paper cites.
The pile: An 800gb dataset of diverse text for language modeling, 2020
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., and Leahy, C · 2020
Cited alongside, same era.
Hippo: Recurrent memory with optimal polynomial projections
Gu, A., Dao, T., Ermon, S., Rudra, A., and Ré, C · 2020
Cited alongside, same era.
Scaling laws for neural language models, 2020
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
Cited alongside, same era.
Transformers are rnns: Fast autoregressive transformers with linear attention, 2020
Katharopoulos, A., Vyas, A., Pappas, N., and Fleuret, F · 2020
Cited alongside, same era.
XCOPA: A multilingual dataset for causal commonsense reasoning
Ponti, E. M., Glavaš, G., Majewska, O., Liu, Q., Vulić, I., and Korhonen, A · 2020
Cited alongside, same era.
Efficiently modeling long sequences with structured state spaces
Flashattention-2: Faster attention with better parallelism and work partitioning, 2023
Dao, T · 2023
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Longnet: Scaling transformers to 1,000,000,000 tokens
Ding, J., Ma, S., Dong, L., Zhang, X., Huang, S., Wang, W., Zheng, N., and Wei, F · 2023
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Hungry hungry hippos: Towards language modeling with state space models, 2023
Fu, D. Y., Dao, T., Saab, K. K., Thomas, A. W., Rudra, A., and Ré, C · 2023
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A framework for few-shot language model evaluation, 12 2023
Gao, L., Tow, J., Abbasi, B., Biderman, S., Black, S., DiPofi, A., Foster, C., Golding, L., Hsu, J., Le Noac’h, A., Li, H., McDonell, K., Muennighoff, N., Ociepa, C., Phang, J., Reynolds, L., Schoelkopf, H., Skowron, A., Sutawika, L., Tang, E., Thite, A., Wang, B., Wang, K., and Zou, A · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T · 2023
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Gu, A., Goel, K., and Re, C · 2021
Cited alongside, same era.
When attention meets fast recurrence: Training language models with reduced compute
Lei, T · 2021
Cited alongside, same era.
Few-shot learning with multilingual language models
Lin, X. V., Mihaylov, T., Artetxe, M., Wang, T., Chen, S., Simig, D., Ott, M., Goyal, N., Bhosale, S., Du, J., Pasunuru, R., Shleifer, S., Koura, P. S., Chaudhary, V., O’Horo, B., Wang, J., Zettlemoyer, L., Kozareva, Z., Diab, M. T., Stoyanov, V., and Li, X · 2021
Cited alongside, same era.
An attention free transformer, 2021
Zhai, S., Talbott, W., Srivastava, N., Huang, C., Goh, H., Zhang, R., and Susskind, J · 2021
Cited alongside, same era.
Gpt-neox-20b: An open-source autoregressive language model, 2022
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., Pieler, M., Prashanth, U. S., Purohit, S., Reynolds, L., Tow, J., Wang, B., and Weinbach, S · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways, 2022
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
Cited alongside, same era.
Flashattention: Fast and memory-efficient exact attention with io-awareness, 2022
Dao, T., Fu, D. Y., Ermon, S., Rudra, A., and Ré, C · 2022
Cited alongside, same era.
Later among the works it cites.
Chatgpt (mar 14 version)
OpenAI · 2023
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Rwkv: Reinventing rnns for the transformer era
Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Cao, H., Cheng, X., Chung, M., Grella, M., GV, K. K., et al · 2023
Later among the works it cites.
Hyena hierarchy: Towards larger convolutional language models
Poli, M., Massaroli, S., Nguyen, E., Fu, D. Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., and Ré, C · 2023
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Exploring the limits of transfer learning with a unified text-to-text transformer, 2023
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2023
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Simplified state space layers for sequence modeling, 2023
Smith, J. T. H., Warrington, A., and Linderman, S. W · 2023
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Retentive network: A successor to transformer for large language models, 2023
Sun, Y., Dong, L., Huang, S., Ma, S., Xia, Y., Xue, J., Wang, J., and Wei, F · 2023
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Llama: Open and efficient foundation language models, 2023
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G · 2023
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Gated linear attention transformers with hardware-efficient training
Yang, S., Wang, B., Shen, Y., Panda, R., and Kim, Y · 2023
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GLM-130b: An open bilingual pre-trained model
Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M., Yang, Z., Xu, Y., Zheng, W., Xia, X., Tam, W. L., Ma, Z., Xue, Y., Zhai, J., Chen, W., Liu, Z., Zhang, P., Dong, Y., and Tang, J · 2023
Later among the works it cites.
Transnormerllm: A faster and better large language model with improved transnormer, 2024
Qin, Z., Li, D., Sun, W., Sun, W., Shen, X., Han, X., Wei, Y., Lv, B., Luo, X., Qiao, Y., and Zhong, Y · 2024
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