X. L. Li, X. Li, T. Ma, A. Malik, C. D. Manning, S. Mirchandani, E. Mitchell, Z. Munyikwa, S. Nair, A. Narayan, D. Narayanan, B. Newman, A. Nie, J. C. Niebles, H. Nilforoshan, J. Nyarko, G. Ogut, L. Orr, I. Papadimitriou, J. S. Park, C. Piech, E. Portelance, C. Potts, A. Raghunathan, R. Reich, H. Ren, F. Rong, Y. Roohani, C. Ruiz, J. Ryan, C. Ré, D. Sadigh, S. Sagawa, K. Santhanam, A. Shih, K. Srinivasan, A. Tamkin, R. Taori, A. W. Thomas, F. Tramèr, R. E. Wang, W. Wang, B. Wu, J. Wu, Y. Wu, S. M. Xie, M. Yasunaga, J. You, M. Zaharia, M. Zhang, T. Zhang, X. Zhang, Y. Zhang, L. Zheng, K. Zhou, and P. Liang. On the opportunities and risks of foundation models, 2022.
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al. Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712, 2023.
A. Bura, D. Rengarajan, D. Kalathil, S. Shakkottai, and J.-F. Chamberland. Learning to cache and caching to learn: Regret analysis of caching algorithms. IEEE/ACM Transactions on Networking, 30(1):18-31, 2021.
W.-C. Chang, F. X. Yu, Y.-W. Chang, Y. Yang, and S. Kumar. Pre-training tasks for embedding-based large-scale retrieval, 2020.
Z. Chang, L. Lei, Z. Zhou, S. Mao, and T. Ristaniemi. Learn to cache: Machine learning for network edge caching in the big data era. IEEE Wireless Communications, 25(3):28-35, 2018.
C. Chen, S. Borgeaud, G. Irving, J.-B. Lespiau, L. Sifre, and J. Jumper. Accelerating large language model decoding with speculative sampling, 2023a.
L. Chen, M. Zaharia, and J. Zou. FrugalGPT: How to use large language models while reducing cost and improving performance, 2023b.
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, I. Stoica, and E. P. Xing. Vicuna: An open-source chatbot impressing GPT-4 with  $90\%$  ChatGPT quality, March 2023. URL https://lmsys.org/blog/2023-03-30-vicuna/.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al. PaLM: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
J. Du, M. Ott, H. Li, X. Zhou, and V. Stoyanov. General purpose text embeddings from pre-trained language models for scalable inference, 2020.
F. Z. Faizal, P. Singh, N. Karamchandani, and S. Moharir. Regret-optimal online caching for adversarial and stochastic arrivals. In Performance Evaluation Methodologies and Tools: 15th EAI International Conference, VALUETOOLS 2022, Virtual Event, November 2022, Proceedings, pages 147-163. Springer, 2023.
W. Fedus, J. Dean, and B. Zoph. A review of sparse expert models in deep learning. arXiv preprint arXiv:2209.01667, 2022.
E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh. GPTQ: Accurate post-training quantization for generative pre-trained transformers, 2023.
A. Gholami, S. Kim, Z. Dong, Z. Yao, M. W. Mahoney, and K. Keutzer. A survey of quantization methods for efficient neural network inference, 2021.
Google. PaLM-2 technical report. 2023.
Y. He, Z. Zhang, F. R. Yu, N. Zhao, H. Yin, V. C. Leung, and Y. Zhang. Deep-reinforcement-learning-based optimization for cache-enabled opportunistic interference alignment wireless networks. IEEE Transactions on Vehicular Technology, 66(11):10433-10445, 2017.
W. Jiang, G. Feng, S. Qin, T. S. P. Yum, and G. Cao. Multi-agent reinforcement learning for efficient content caching in mobile d2d networks. IEEE Transactions on Wireless Communications, 18(3): 1610-1622, 2019.