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This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM).
Autoprompt: Eliciting knowledge from language models with automatically generated prompts,
T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, S. Singh, · 2020
Earlier work this paper cites.
Training verifiers to solve math word problems,
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, et al., · 2021
Earlier work this paper cites.
Learning how to ask: Querying lms with mixtures of soft prompts,
G. Qin, J. Eisner, · 2021
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning,
B. Lester, R. Al-Rfou, N. Constant, · 2021
Earlier work this paper cites.
Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts,
T. Wu, M. Terry, C. J. Cai, · 2022
Earlier work this paper cites.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity,
Y. Lu, M. Bartolo, A. Moore, S. Riedel, P. Stenetorp, · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback,
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al., · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models,
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. H. Chi, Q. V. Le, D. Zhou, et al., · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners,
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, Y. Iwasawa, · 2022
Cited alongside, same era.
Self-instruct: Aligning language model with self generated instructions,
Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, H. Hajishirzi, · 2022
Cited alongside, same era.
Least-to-most prompting enables complex reasoning in large language models,
D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, O. Bousquet, Q. Le, E. Chi, · 2022
Cited alongside, same era.
Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning,
P. Lu, L. Qiu, K.-W. Chang, Y. N. Wu, S.-C. Zhu, T. Rajpurohit, P. Clark, A. Kalyan, · 2022
Instruction induction: From few examples to natural language task descriptions,
O. Honovich, U. Shaham, S. R. Bowman, O. Levy, · 2022
Later among the works it cites.
Chatgpt outperforms crowd-workers for text-annotation tasks,
F. Gilardi, M. Alizadeh, M. Kubli, · 2023
Closest in time.
Large language models are human-level prompt engineers,
Y. Zhou, A. I. Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan, J. Ba, · 2023
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Tempera: Test-time prompt editing via reinforcement learning,
T. Zhang, X. Wang, D. Zhou, D. Schuurmans, J. E. Gonzalez, · 2023
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Automatic prompt optimization with” gradient descent” and beam search,
R. Pryzant, D. Iter, J. Li, Y. T. Lee, C. Zhu, M. Zeng, · 2023
Closest in time.
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Cited alongside, same era.
Rlprompt: Optimizing discrete text prompts with reinforcement learning,
M. Deng, J. Wang, C.-P. Hsieh, Y. Wang, H. Guo, T. Shu, M. Song, E. P. Xing, Z. Hu, · 2022
Cited alongside, same era.
Optimizing prompts for text-to-image generation,
Y. Hao, Z. Chi, L. Dong, F. Wei, · 2022
Cited alongside, same era.
Star: Bootstrapping reasoning with reasoning,
E. Zelikman, Y. Wu, J. Mu, N. Goodman,
Cited in the paper.
Reprompting: Automated chain-of-thought prompt inference through gibbs sampling,
W. Xu, A. Banburski-Fahey, N. Jojic, · 2023
Closest in time.
Large language model guided tree-of-thought,
J. Long, · 2023
Closest in time.