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The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates.
Recursive deep models for semantic compositionality over a sentiment treebank
R. Socher, A. Perelygin, J. Wu, J. Chuang, et al · 2013
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, et al · 2014
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Character-level convolutional networks for text classification
X. Zhang, J. Zhao, and Y. LeCun · 2015
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Mawps: A math word problem repository
R. Koncel-Kedziorski, S. Roy, A. Amini, N. Kushman, and H. Hajishirzi · 2016
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
A. Talmor, J. Herzig, N. Lourie, and J. Berant · 2019
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh · 2020
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Training verifiers to solve math word problems
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, et al · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
M. Geva, D. Khashabi, E. Segal, T. Khot, et al · 2021
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What disease does this patient have? a large-scale open domain question answering dataset from medical exams
D. Jin, E. Pan, N. Oufattole, et al · 2021
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The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
X. L. Li and P. Liang · 2021
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
M. Deng, J. Wang, et al · 2022
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Instruction induction: From few examples to natural language task descriptions
O. Honovich, U. Shaham, S. R. Bowman, and O. Levy · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Y. Lu, M. Bartolo, A. Moore, S. Riedel, et al · 2022
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What makes good in-context examples for gpt-3?
J. Liu, D. Shen, Y. Zhang, W. B. Dolan, et al · 2022
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Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering
A. Pal, L. K. Umapathi, and M. Sankarasubbu · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, et al · 2022
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Gps: Genetic prompt search for efficient few-shot learning
H. Xu, Y. Chen, Y. Du, and others · 2022
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Active example selection for in-context learning
Y. Zhang, S. Feng, and C. Tan · 2022
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LLMs are human-level prompt engineers
Y. Zhou, A. I. Muresanu, Z. Han, et al · 2022
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Tempera: Test-time prompting via reinforcement learning
T. Zhang, X. Wang, D. Zhou, et al · 2022
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Black-box prompt optimization: Aligning LLMs without model training
J. Cheng, X. Liu, et al · 2023
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Mapo: Boosting large language model performance with model-adaptive prompt optimization
Y. Chen, Z. Wen, G. Fan, Z. Chen, et al · 2023
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Pace: Improving prompt with actor-critic editing for large language model
Y. Dong, K. Luo, and others · 2023
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Promptbreeder: Self-referential self-improvement via prompt evolution
C. Fernando, D. Banarse, H. Michalewski, S. Osindero, and T. Rocktäschel · 2023
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A systematic survey of prompt engineering on vision-language foundation models
J. Gu, Z. Han, S. Chen, A. Beirami, et al · 2023
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Connecting LLMs with evolutionary algorithms yields powerful prompt optimizers
Q. Guo, R. Wang, J. Guo, B. Li, et al · 2023
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Automatic engineering of long prompts
C.i Hsieh, S. Si, F. Yu, and I. Dhillon · 2023
Cited alongside, same era.
Evoke: Evoking critical thinking abilities in LLMs via reviewer-author prompt editing
X. Hu, P. Tang, S. Zuo, Z. Wang, et al · 2023
Cited alongside, same era.
Segment anything
A. Kirillov, E. Mintun, N. Ravi, H. Mao, et al · 2023
Cited alongside, same era.
Multiprompter: Cooperative prompt optimization with multi-agent reinforcement learning
D. Kim, S. Sohn, L. Logeswaran, D. Shim, et al · 2023
Cited alongside, same era.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
P. Liu, W. Yuan, J. Fu, et al · 2023
Cited alongside, same era.
Active learning principles for in-context learning with LLMs
K. Margatina, T. Schick, N. Aletras, and J. Dwivedi · 2023
Morl-prompt: An empirical analysis of multi-objective reinforcement learning for discrete prompt optimization
Y. Jafari, D. Mekala, R. Yu, et al · 2024
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Prewrite: Prompt rewriting with reinforcement learning
W. Kong, S. A. Hombaiah, M. Zhang, Q. Mei, and M. Bendersky · 2024
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Stableprompt: Automatic prompt tuning using reinforcement learning for LLMs
M. Kwon, G. Kim, J. Kim, H. Lee, and J. Kim · 2024
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Learning from contrastive prompts: Automated optimization and adaptation
M. Li, K. Aggarwal, Y. Xie, A. Ahmad, and S. Lau · 2024
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Prompt compression for LLMs: A survey
Z. Li, Y. Liu, Y. Su, and N. Collier · 2024
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Gpt understands, too
X. Liu, Y. Zheng, et al · 2024
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Cited alongside, same era.
Grips: Gradient-free, edit-based instruction search for prompting LLMs
A. Prasad, P. Hase, X. Zhou, and M. Bansal · 2023
Cited alongside, same era.
Automatic prompt optimization with “gradient descent” and beam search
R. Pryzant, D. Iter, et al · 2023
Cited alongside, same era.
Kosmos-2: Grounding multimodal LLMs to the world
Z. Peng, W. Wang, et al · 2023
Cited alongside, same era.
Measuring and narrowing the compositionality gap in language models
O. Press, M. Zhang, et al · 2023
Cited alongside, same era.
Neuroprompts: An adaptive framework to optimize prompts for text-to-image generation
S. Rosenman, V. Lal, and P. Howard · 2023
Cited alongside, same era.
Query-dependent prompt evaluation and optimization with offline inverse rl
H. Sun, A. Hüyük, and M. van der Schaar · 2023
Cited alongside, same era.
Prompt optimization via adversarial in-context learning
D. Long, Y. Zhao, H. Brown, and others · 2024
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Improving text-to-image consistency via automatic prompt optimization
O. Mañas, P. Astolfi, et al · 2024
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Eureka: Human-level reward design via coding LLMs
Y. J. Ma, W. Liang, G. Wang, et al · 2024
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Fast prompt alignment for text-to-image generation
K. Mrini, H. Lu, L. Yang, W. Huang, et al · 2024
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Self-refine: Iterative refinement with self-feedback
A. Madaan, N. Tandon, P. Gupta, et al · 2024
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Dynamic prompt optimizing for text-to-image generation
W. Mo, T. Zhang, Y. Bai, B. Su, et al · 2024
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Optimizing negative prompts for enhanced aesthetics and fidelity in text-to-image generation
M. Ogezi and N. Shi · 2024
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Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting
M. Sclar, Y. Choi, Y. Tsvetkov, and A. Suhr · 2024
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A systematic survey of prompt engineering in LLMs: Techniques and applications
P. Sahoo, A. K. Singh, S. Saha, V. Jain, et al · 2024
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A survey of prompt engineering methods in LLMs for different nlp tasks
S. Vatsal and H. Dubey · 2024
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Strago: Harnessing strategic guidance for prompt optimization
Y. Wu, Y. Gao, B. B. Zhu, Z. Zhou, et al · 2024
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Promptcharm: Text-to-image generation through multi-modal prompting and refinement
Z. Wang, Y. Huang, D. Song, L. Ma, and T. Zhang · 2024
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Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Y. Wen, N. Jain, J. Kirchenbauer, M. Goldblum, et al · 2024
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Promptagent: Strategic planning with language models enables expert-level prompt optimization
X. Wang, C. Li, Z. Wang, F. Bai, et al · 2024
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Efficient LLMs: A survey
Z. Wan, X. Wang, C. Liu, S. Alam, et al · 2024
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Tipo: Text to image with text presampling for prompt optimization
S. Yeh, S. Park, G. Oh, et al · 2024
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Ampo: Automatic multi-branched prompt optimization
S. Yang, Y. Wu, Y. Gao, Z. Zhou, et al · 2024
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LLMs as optimizers
C. Yang, X. Wang, Y. Lu, H. Liu, et al · 2024
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Mopo: Multi-objective prompt optimization for affective text generation
Y. Menchaca Resendiz and R. Klinger · 2025
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Soft prompt tuning for augmenting dense retrieval with LLMs
Z. Peng, X. Wu, Q. Wang, and Y. Fang · 2025
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Mixture-of-experts in prompt optimization
R. Wang, S. An, M. Cheng, T. Zhou, et al · 2025
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The rise and potential of large language model based agents: A survey
Z. Xi, W. Chen, X. Guo, W. He, et al · 2025
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