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With the blossom of large language models (LLMs), inference efficiency becomes increasingly important.
Optimal brain damage
LeCun, Y., Denker, J., and Solla, S. (1989) · 1989
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Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · 1996
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020) · 2001
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Interleaving for combating bursts of errors
Shi, Y., Zhang, X. M., Ni, Z.-C., and Ansari, N. (2004) · 2004
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Beam search strategies for neural machine translation
Freitag, M. and Al-Onaizan, Y. (2017) · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Ling, W., Yogatama, D., Dyer, C., and Blunsom, P. (2017) · 2017
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Get to the point: Summarization with pointer-generator networks
See, A., Liu, P. J., and Manning, C. D. (2017) · 2017
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Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph
Saha, A., Pahuja, V., Khapra, M. M., Sankaranarayanan, K., and Chandar, S. (2018) · 2018
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Coqa: A conversational question answering challenge
Reddy, S., Chen, D., and Manning, C. D. (2019) · 2019
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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) · 2020
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W. (2021) · 2021
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J. (2021) · 2021
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Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., and Berant, J. (2021) · 2021
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J. (2021) · 2021
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Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Castro-Ros, A., Pellat, M., Robinson, K., Valter, D., Narang, S., Mishra, G., Yu, A., Zhao, V., Huang, Y., Dai, A., Yu, H., Petrov, S., Chi, E. H., Dean, J., Devlin, J., Roberts, A., Zhou, D., Le, Q. V., and Wei, J. (2022) · 2022
Cited alongside, same era.
The lazy neuron phenomenon: On emergence of activation sparsity in transformers
Li, Z., You, C., Bhojanapalli, S., Li, D., Rawat, A. S., Reddi, S. J., Ye, K., Chern, F., Yu, F., Guo, R., and Kumar, S. (2022) · 2022
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Truthfulqa: Measuring how models mimic human falsehoods
Lin, S., Hilton, J., and Evans, O. (2022) · 2022
Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y. (2023) · 2023
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Llm-pruner: On the structural pruning of large language models
Ma, X., Fang, G., and Wang, X. (2023) · 2023
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The truth is in there: Improving reasoning in language models with layer-selective rank reduction
Sharma, P., Ash, J. T., and Misra, D. (2023) · 2023
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Powerinfer: Fast large language model serving with a consumer-grade gpu
Song, Y., Mi, Z., Xie, H., and Chen, H. (2023) · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X. E., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, T. (2023) · 2023
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Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. (2022) · 2022
Cited alongside, same era.
Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., Mihaylov, T., Ott, M., Shleifer, S., Shuster, K., Simig, D., Koura, P. S., Sridhar, A., Wang, T., and Zettlemoyer, L. (2022) · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
bench authors, B. (2023) · 2023
Cited alongside, same era.
Accelerating large language model decoding with speculative sampling
Chen, C., Borgeaud, S., Irving, G., Lespiau, J.-B., Sifre, L., and Jumper, J. (2023) · 2023
Cited alongside, same era.
Sparsegpt: Massive language models can be accurately pruned in one-shot
Frantar, E. and Alistarh, D. (2023) · 2023
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Speculative decoding with big little decoder
Kim, S., Mangalam, K., Moon, S., Malik, J., Mahoney, M. W., Gholami, A., and Keutzer, K. (2023) · 2023
Cited alongside, same era.
Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation
Liu, J., Xia, C. S., Wang, Y., and Zhang, L. (2023a)
Cited in the paper.
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Dola: Decoding by contrasting layers improves factuality in large language models
Chuang, Y.-S., Xie, Y., Luo, H., Kim, Y., Glass, J., and He, P. (2024) · 2024
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Prompt-prompted mixture of experts for efficient llm generation
Dong, H., Chen, B., and Chi, Y. (2024) · 2024
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Cats: Contextually-aware thresholding for sparsity in large language models
Lee, J.-Y., Lee, D., Zhang, G., Tiwari, M., and Mirhoseini, A. (2024) · 2024
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Gpt-4 technical report
OpenAI, Achiam, J., et al. (2024) · 2024
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Gemini: A family of highly capable multimodal models
Team, G., Anil, R., et al. (2024) · 2024
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Sheared llama: Accelerating language model pre-training via structured pruning
Xia, M., Gao, T., Zeng, Z., and Chen, D. (2024) · 2024
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