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Large language models have demonstrated remarkable capabilities, but their performance is heavily reliant on effective prompt engineering.
Efficient selectivity and backup operators in monte-carlo tree search
Coulom, R · 2006
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Bayesian optimization of combinatorial structures
Baptista, R. and Poloczek, M · 2018
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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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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., and Singh, S · 2020
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True few-shot learning with language models
Perez, E., Kiela, D., and Cho, K · 2021
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Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces
Wan, X., Nguyen, V., Ha, H., Ru, B., Lu, C., and Osborne, M. A · 2021
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Calibrate before use: Improving few-shot performance of language models
Zhao, Z., Wallace, E., Feng, S., Klein, D., and Singh, S · 2021
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On the relation between sensitivity and accuracy in in-context learning
Chen, Y., Zhao, C., Yu, Z., McKeown, K., and He, H · 2022
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Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization
Daulton, S., Wan, X., Eriksson, D., Balandat, M., Osborne, M. A., and Bakshy, E · 2022
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RLPrompt: Optimizing discrete text prompts with reinforcement learning
Deng, M., Wang, J., Hsieh, C.-P., Wang, Y., Guo, H., Shu, T., Song, M., Xing, E., and Hu, Z · 2022
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Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp
Khattab, O., Santhanam, K., Li, X. L., Hall, D., Liang, P., Potts, C., and Zaharia, M · 2022
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Kim, H. J., Cho, H., Kim, J., Kim, T., Yoo, K. M., and Lee, S.-g · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Lu, Y., Bartolo, M., Moore, A., Riedel, S., and Stenetorp, P · 2022
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Learning to retrieve prompts for in-context learning
Rubin, O., Herzig, J., and Berant, J · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them
Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q. V., Chi, E. H., Zhou, D., et al · 2022
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Gps: Genetic prompt search for efficient few-shot learning
Xu, H., Chen, Y., Du, Y., Shao, N., Wang, Y., Li, H., and Yang, Z · 2022
Cited alongside, same era.
Active example selection for in-context learning
Zhang, Y., Feng, S., and Tan, C · 2022
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al · 2023
Cited alongside, same era.
Instructzero: Efficient instruction optimization for black-box large language models
Autohint: Automatic prompt optimization with hint generation
Sun, H., Li, X., Xu, Y., Homma, Y., Cao, Q., Wu, M., Jiao, J., and Charles, D · 2023
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Better zero-shot reasoning with self-adaptive prompting
Wan, X., Sun, R., Dai, H., Arik, S., and Pfister, T · 2023
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Universal self-adaptive prompting
Wan, X., Sun, R., Nakhost, H., Dai, H., Eisenschlos, J., Arik, S., and Pfister, T · 2023
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Self-adaptive in-context learning: An information compression perspective for in-context example selection and ordering
Wu, Z., Wang, Y., Ye, J., and Kong, L · 2023
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Survival of the most influential prompts: Efficient black-box prompt search via clustering and pruning
Zhou, H., Wan, X., Vulić, I., and Korhonen, A · 2023
Later among the works it cites.
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Chen, L., Chen, J., Goldstein, T., Huang, H., and Zhou, T · 2023
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 · 2023
Cited alongside, same era.
Prompt optimization via adversarial in-context learning
Do, X. L., Zhao, Y., Brown, H., Xie, Y., Zhao, J. X., Chen, N. F., Kawaguchi, K., Xie, M. Q., and He, J · 2023
Cited alongside, same era.
Promptbreeder: Self-referential self-improvement via prompt evolution
Fernando, C., Banarse, D., Michalewski, H., Osindero, S., and Rocktäschel, T · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini team, Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
Cited alongside, same era.
Instruction induction: From few examples to natural language task descriptions
Honovich, O., Shaham, U., Bowman, S. R., and Levy, O · 2023
Cited alongside, same era.
Automatic engineering of long prompts
Hsieh, C.-J., Si, S., Yu, F. X., and Dhillon, I. S · 2023
Cited alongside, same era.
Use your instinct: Instruction optimization using neural bandits coupled with transformers
Lin, X., Wu, Z., Dai, Z., Hu, W., Shu, Y., Ng, S.-K., Jaillet, P., and Low, B. K. H · 2023
Cited alongside, same era.
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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DSPy: Compiling declarative language model calls into self-improving pipelines
Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., Vardhamanan, S., Haq, S., Sharma, A., Joshi, T. T., Moazam, H., et al · 2024
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Gecko: Versatile text embeddings distilled from large language models
Lee, J., Dai, Z., Ren, X., Chen, B., Cer, D., Cole, J. R., Hui, K., Boratko, M., Kapadia, R., Ding, W., et al · 2024
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State of what art? a call for multi-prompt llm evaluation
Mizrahi, M., Kaplan, G., Malkin, D., Dror, R., Shahaf, D., and Stanovsky, G · 2024
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Optimizing instructions and demonstrations for multi-stage language model programs
Opsahl-Ong, K., Ryan, M. J., Purtell, J., Broman, D., Potts, C., Zaharia, M., and Khattab, O · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reid, M., Savinov, N., Teplyashin, D., Lepikhin, D., Lillicrap, T., Alayrac, J.-b., Soricut, R., Lazaridou, A., Firat, O., Schrittwieser, J., et al · 2024
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A systematic survey of prompt engineering in large language models: Techniques and applications
Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., and Chadha, A · 2024
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Towards truly zero-shot compositional visual reasoning with llms as programmers
Stanić, A., Caelles, S., and Tschannen, M · 2024
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Prompt optimization with ease? efficient ordering-aware automated selection of exemplars
Wu, Z., Lin, X., Dai, Z., Hu, W., Shu, Y., Ng, S.-K., Jaillet, P., and Low, B. K. H · 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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What makes good examples for visual in-context learning?
Zhang, Y., Zhou, K., and Liu, Z · 2024
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