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Large language models (LLMs) have shown remarkable instruction-following capabilities and achieved impressive performances in various applications.
Benchmarking optimization software with performance profiles
Dolan, E. D. and Moré, J. J · 2002
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ROUGE: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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Gaussian Processes for Machine Learning
Rasmussen, C. E. and Williams, C. K. I · 2006
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Algorithms for hyper-parameter optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B · 2011
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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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
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
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On exact computation with an infinitely wide neural net
Arora, S., Du, S. S., Hu, W., Li, Z., Salakhutdinov, R. R., and Wang, R · 2019
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Scalable global optimization via local bayesian optimization
Eriksson, D., Pearce, M., Gardner, J., Turner, R. D., and Poloczek, M · 2019
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SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Gliwa, B., Mochol, I., Biesek, M., and Wawer, A · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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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
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Bandit Algorithms
Lattimore, T. and Szepesvári, C · 2020
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Eliciting knowledge from language models using automatically generated prompts
Shin, T., Razeghi, Y., IV, R. L. L., Wallace, E., and Singh, S · 2020
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Neural contextual bandits with UCB-based exploration
Zhou, D., Li, L., and Gu, Q · 2020
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Large language models are human-level prompt engineers
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J · 2020
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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., et al · 2021
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Gu, Q., Karbasi, A., Khosravi, K., Mirrokni, V., and Zhou, D · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
Cited alongside, same era.
An empirical study of neural kernel bandits
Lisicki, M., Afkanpour, A., and Taylor, G. W · 2021
Cited alongside, same era.
Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al · 2023
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Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
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Black-box prompt learning for pre-trained language models
Diao, S., Huang, Z., Xu, R., Li, X., Yong, L., Zhou, X., and Zhang, T · 2023
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Promptbreeder: Self-referential self-improvement via prompt evolution
Fernando, C., Banarse, D., Michalewski, H., Osindero, S., and Rocktäschel, T · 2023
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Bayesian optimization
Garnett, R · 2023
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Mishra, S., Khashabi, D., Baral, C., Choi, Y., and Hajishirzi, H · 2021
Cited alongside, same era.
Are NLP models really able to solve simple math word problems?
Patel, A., Bhattamishra, S., and Goyal, N · 2021
Cited alongside, same era.
Prompt programming for large language models: Beyond the few-shot paradigm
Reynolds, L. and McDonell, K · 2021
Cited alongside, same era.
BORE: Bayesian optimization by density-ratio estimation
Tiao, L. C., Klein, A., Seeger, M. W., Bonilla, E. V., Archambeau, C., and Ramos, F · 2021
Cited alongside, same era.
Neural Thompson sampling
Zhang, W., Zhou, D., Li, L., and Gu, Q · 2021
Cited alongside, same era.
Factual probing is [MASK]: Learning vs. learning to recall
Zhong, Z., Friedman, D., and Chen, D · 2021
Cited alongside, same era.
Clip-tuning: Towards derivative-free prompt learning with a mixture of rewards
Chai, Y., Wang, S., Sun, Y., Tian, H., Wu, H., and Wang, H · 2022
Cited alongside, same era.
Open LLM leaderboard
Huggingface · 2023
Closest in time.
AlpacaEval: An automatic evaluator of instruction-following models
Li, X., Zhang, T., Dubois, Y., Taori, R., Gulrajani, I., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 2023
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Chatbot arena leaderboard
LMSYS · 2023
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GrIPS: Gradient-free, edit-based instruction search for prompting large language models
Prasad, A., Hase, P., Zhou, X., and Bansal, M · 2023
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Automatic prompt optimization with “gradient descent” and beam search
Pryzant, R., Iter, D., Li, J., Lee, Y. T., Zhu, C., and Zeng, M · 2023
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Toward human readable prompt tuning: Kubrick’s the shining is a good movie, and a good prompt too?
Shi, W., Han, X., Gonen, H., Holtzman, A., Tsvetkov, Y., and Zettlemoyer, L · 2023
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LLaMA: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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A survey of large language models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al · 2023
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Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
Guo, Q., Wang, R., Guo, J., Li, B., Song, K., Tan, X., Liu, G., Bian, J., and Yang, Y · 2024
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WizardLM: Empowering large language models to follow complex instructions
Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., and Jiang, D · 2024
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2024
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