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Recent advances with large language models (LLM) illustrate their diverse capabilities.
Estimation of probabilities from sparse data for the language model component of a speech recognizer
Katz, S · 1987
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Crafting papers on machine learning
Langley, P · 2000
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The unreasonable effectiveness of data
Halevy, A., Norvig, P., and Pereira, F · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Applying the roofline model
Ofenbeck, G., Steinmann, R., Caparros, V., Spampinato, D. G., and Püschel, M · 2014
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library, 2019
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
The pile: An 800gb dataset of diverse text for language modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T. B., Song, D., Erlingsson, U., et al · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
Gptq: Accurate post-training quantization for generative pre-trained transformers
Frantar, E., Ashkboos, S., Hoefler, T., and Alistarh, D · 2022
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The stack: 3 tb of permissively licensed source code
Kocetkov, D., Li, R., Allal, L. B., Li, J., Mou, C., Ferrandis, C. M., Jernite, Y., Mitchell, M., Hughes, S., Wolf, T., et al · 2022
Later among the works it cites.
Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y · 2022
Later among the works it cites.
Nvidia RTX 4090 GPU architecture, 2022
NVIDIA · 2022
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Accelerating large language model decoding with speculative sampling
Chen, C., Borgeaud, S., Irving, G., Lespiau, J.-B., Sifre, L., and Jumper, J · 2023
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Cited alongside, same era.
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
Cited alongside, same era.
Flashattention: Fast and memory-efficient exact attention with io-awareness
Dao, T., Fu, D., Ermon, S., Rudra, A., and Ré, C · 2022
Cited alongside, same era.
Llm. int8 (): 8-bit matrix multiplication for transformers at scale
Dettmers, T., Lewis, M., Belkada, Y., and Zettlemoyer, L · 2022
Cited alongside, same era.
Closest in time.
Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.
High-throughput generative inference of large language models with a single gpu
Sheng, Y., Zheng, L., Yuan, B., Li, Z., Ryabinin, M., Fu, D. Y., Xie, Z., Chen, B., Barrett, C., Gonzalez, J. E., et al · 2023
Closest in time.
Tabi: An efficient multi-level inference system for large language models
Wang, Y., Chen, K., Tan, H., and Guo, K · 2023
Closest in time.