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Large Language Models (LLMs) have achieved remarkable advancements, but their monolithic nature presents challenges in terms of scalability, cost, and customization.
Minimum bayes-risk decoding for statistical machine translation
Kumar, S. and Byrne, B · 2004
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Visualizing data using t-sne
Van der Maaten, L. and Hinton, G · 2008
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Active learning literature survey
Settles, B · 2009
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Making machine learning robust against adversarial inputs
Goodfellow, I., McDaniel, P., and Papernot, N · 2018
Earlier work this paper cites.
The relative performance of ensemble methods with deep convolutional neural networks for image classification
Ju, C., Bibaut, A., and van der Laan, M · 2018
Earlier work this paper cites.
Efficient randomized defense against adversarial attacks in deep convolutional neural networks
Sheikholeslami, F., Jain, S., and Giannakis, G. B · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Gshard: Scaling giant models with conditional computation and automatic sharding
Lepikhin, D., Lee, H., Xu, Y., Chen, D., Firat, O., Huang, Y., Krikun, M., Shazeer, N., and Chen, Z · 2020
Earlier work this paper cites.
Minimum uncertainty based detection of adversaries in deep neural networks
Sheikholeslami, F., Jain, S., and Giannakis, G. B · 2020
Earlier work this paper cites.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, 2020
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors · 2020
Earlier work this paper cites.
The flores-101 evaluation benchmark for low-resource and multilingual machine translation
Goyal, N., Gao, C., Chaudhary, V., Chen, P.-J., Wenzek, G., Ju, D., Krishnan, S., Ranzato, M., Guzmán, F., and Fan, A · 2021
Earlier work this paper cites.
Efficacy of bayesian neural networks in active learning
Rakesh, V. and Jain, S · 2021
Earlier work this paper cites.
Branch-train-merge: Embarrassingly parallel training of expert language models, 2022
Li, M., Gururangan, S., Dettmers, T., Lewis, M., Althoff, T., Smith, N. A., and Zettlemoyer, L · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Cited alongside, same era.
Ultrafeedback: Boosting language models with high-quality feedback, 2023
Cui, G., Yuan, L., Ding, N., Yao, G., Zhu, W., Ni, Y., Xie, G., Liu, Z., and Sun, M · 2023
Cited alongside, same era.
Scaling expert language models with unsupervised domain discovery, 2023
Gururangan, S., Li, M., Lewis, M., Shi, W., Althoff, T., Smith, N. A., and Zettlemoyer, L · 2023
Cited alongside, same era.
Exploring the benefits of training expert language models over instruction tuning, 2023
Jang, J., Kim, S., Ye, S., Kim, D., Logeswaran, L., Lee, M., Lee, K., and Seo, M · 2023
Cited alongside, same era.
Chatbot arena: An open platform for evaluating llms by human preference, 2024
Chiang, W.-L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., Zhang, H., Zhu, B., Jordan, M., Gonzalez, J. E., and Stoica, I · 2024
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
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From crowdsourced data to high-quality benchmarks: Arena-hard and benchbuilder pipeline, 2024
Li, T., Chiang, W.-L., Frick, E., Dunlap, L., Wu, T., Zhu, B., Gonzalez, J. E., and Stoica, I · 2024
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Lmsys - chatbot arena human preference predictions
lin Chiang, W., Zheng, L., Dunlap, L., Gonzalez, J. E., Stoica, I., Mooney, P., Dane, S., Howard, A., and Keating, N · 2024
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Openassistant conversations – democratizing large language model alignment, 2023
Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z.-R., Stevens, K., Barhoum, A., Duc, N. M., Stanley, O., Nagyfi, R., ES, S., Suri, S., Glushkov, D., Dantuluri, A., Maguire, A., Schuhmann, C., Nguyen, H., and Mattick, A · 2023
Cited alongside, same era.
Routing to the expert: Efficient reward-guided ensemble of large language models
Lu, K., Yuan, H., Lin, R., Lin, J., Yuan, Z., Zhou, C., and Zhou, J · 2023
Cited alongside, same era.
Mteb: Massive text embedding benchmark, 2023
Muennighoff, N., Tazi, N., Magne, L., and Reimers, N · 2023
Cited alongside, same era.
Llm routing with benchmark datasets
Shnitzer, T., Ou, A., Silva, M., Soule, K., Sun, Y., Solomon, J., Thompson, N., and Yurochkin, M · 2023
Cited alongside, same era.
x-self-instruct-seed-32, 5 2023
Systems, S · 2023
Cited alongside, same era.
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., et al · 2023
Cited alongside, same era.
Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., Zhang, H., Gonzalez, J. E., and Stoica, I · 2023
Cited alongside, same era.
Luo, Y., Yang, Z., Meng, F., Li, Y., Zhou, J., and Zhang, Y · 2024
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Sambanova sn40l rdu: Breaking the barrier of trillion+ parameter scale gen ai computing
Prabhakar, R · 2024
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Sambanova sn40l: Scaling the ai memory wall with dataflow and composition of experts, 2024
Prabhakar, R., Sivaramakrishnan, R., Gandhi, D., Du, Y., Wang, M., Song, X., Zhang, K., Gao, T., Wang, A., Li, K., Sheng, Y., Brot, J., Sokolov, D., Vivek, A., Leung, C., Sabnis, A., Bai, J., Zhao, T., Gottscho, M., Jackson, D., Luttrell, M., Shah, M. K., Chen, E., Liang, K., Jain, S., Thakker, U., Huang, D., Jairath, S., Brown, K. J., and Olukotun, K · 2024
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Constructing domain-specific evaluation sets for llm-as-a-judge
Raju, R., Jain, S., Li, B., Li, J., and Thakkar, U · 2024
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Aya dataset: An open-access collection for multilingual instruction tuning, 2024
Singh, S., Vargus, F., Dsouza, D., Karlsson, B. F., Mahendiran, A., Ko, W.-Y., Shandilya, H., Patel, J., Mataciunas, D., OMahony, L., Zhang, M., Hettiarachchi, R., Wilson, J., Machado, M., Moura, L. S., Krzemiński, D., Fadaei, H., Ergün, I., Okoh, I., Alaagib, A., Mudannayake, O., Alyafeai, Z., Chien, V. M., Ruder, S., Guthikonda, S., Alghamdi, E. A., Gehrmann, S., Muennighoff, N., Bartolo, M., Kreutzer, J., Üstün, A., Fadaee, M., and Hooker, S · 2024
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Branch-train-mix: Mixing expert llms into a mixture-of-experts llm, 2024
Sukhbaatar, S., Golovneva, O., Sharma, V., Xu, H., Lin, X. V., Rozière, B., Kahn, J., Li, D., tau Yih, W., Weston, J., and Li, X · 2024
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Gemma 2: Improving open language models at a practical size
Team, G., Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al · 2024
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Wang, Y., Ma, X., Zhang, G., Ni, Y., Chandra, A., Guo, S., Ren, W., Arulraj, A., He, X., Jiang, Z., Li, T., Ku, M., Wang, K., Zhuang, A., Fan, R., Yue, X., and Chen, W · 2024
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