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Due to rapid advancements in the development of Large Language Models (LLMs), programming these models with prompts has recently gained significant attention.
Pattern-oriented software architecture, patterns for concurrent and networked objects , volume 2
D. C. Schmidt, M. Stal, H. Rohnert, and F. Buschmann · 2013
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Weapons of math destruction: How big data increases inequality and threatens democracy
C. O’neil · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Language models as knowledge bases?
F. Petroni, T. Rocktäschel, P. Lewis, A. Bakhtin, Y. Wu, A. H. Miller, and S. Riedel · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al · 2019
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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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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On the dangers of stochastic parrots: Can language models be too big?
E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell · 2021
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On the opportunities and risks of foundation models
R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, et al · 2021
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Metaicl: Learning to learn in context
S. Min, M. Lewis, L. Zettlemoyer, and H. Hajishirzi · 2021
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True few-shot learning with language models
E. Perez, D. Kiela, and K. Cho · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
L. Reynolds and K. McDonell · 2021
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Learning to retrieve prompts for in-context learning
O. Rubin, J. Herzig, and J. Berant · 2021
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It’s not just size that matters: Small language models are also few-shot learners, 2021
T. Schick and H. Schütze · 2021
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Calibrate before use: Improving few-shot performance of language models
Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh · 2021
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Ask me anything: A simple strategy for prompting language models
S. Arora, A. Narayan, M. F. Chen, L. Orr, N. Guha, K. Bhatia, I. Chami, F. Sala, and C. Ré · 2022
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A survey for in-context learning
Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, and Z. Sui · 2022
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Complexity-based prompting for multi-step reasoning
Y. Fu, H. Peng, A. Sabharwal, P. Clark, and T. Khot · 2022
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Inner monologue: Embodied reasoning through planning with language models
W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar, et al · 2022
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Large language models are zero-shot reasoners
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022
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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
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On second thought, let’s not think step by step! bias and toxicity in zero-shot reasoning
O. Shaikh, H. Zhang, W. Held, M. Bernstein, and D. Yang · 2022
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LaMDA: Language models for dialog applications
R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, et al · 2022
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A systematic evaluation of large language models of code
F. F. Xu, U. Alon, G. Neubig, and V. J. Hellendoorn · 2022
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Complementary explanations for effective in-context learning
X. Ye, S. Iyer, A. Celikyilmaz, V. Stoyanov, G. Durrett, and R. Pasunuru · 2022
Cited alongside, same era.
Large language models are human-level prompt engineers
Y. Zhou, A. I. Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan, and J. Ba · 2022
Cited alongside, same era.
Conversational health agents: A personalized LLM-powered agent framework
M. Abbasian, I. Azimi, A. M. Rahmani, and R. Jain · 2023
Cited alongside, same era.
Y. Bang, S. Cahyawijaya, N. Lee, W. Dai, D. Su, B. Wilie, H. Lovenia, Z. Ji, T. Yu, W. Chung, et al · 2023
Cited alongside, same era.
Code llama: Open foundation models for code
B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al · 2023
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Towards a catalog of prompt patterns to enhance the discipline of prompt engineering
D. C. Schmidt, J. Spencer-Smith, Q. Fu, and J. White · 2023
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M. Sclar, Y. Choi, Y. Tsvetkov, and A. Suhr · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
N. Shinn, B. Labash, and A. Gopinath · 2023
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Automatic prompt augmentation and selection with chain-of-thought from labeled data
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M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, L. Gianinazzi, J. Gajda, T. Lehmann, M. Podstawski, H. Niewiadomski, P. Nyczyk, et al · 2023
Cited alongside, same era.
Prompting large language models with the socratic method
E. Y. Chang · 2023
Cited alongside, same era.
Teaching large language models to self-debug
X. Chen, M. Lin, N. Schärli, and D. Zhou · 2023
Cited alongside, same era.
PaLM: Scaling language modeling with pathways
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al · 2023
Cited alongside, same era.
Chatlaw: Open-source legal large language model with integrated external knowledge bases
J. Cui, Z. Li, Y. Yan, B. Chen, and L. Yuan · 2023
Cited alongside, same era.
Multilingual jailbreak challenges in large language models
Y. Deng, W. Zhang, S. J. Pan, and L. Bing · 2023
Cited alongside, same era.
Revisit input perturbation problems for llms: A unified robustness evaluation framework for noisy slot filling task
G. Dong, J. Zhao, T. Hui, D. Guo, W. Wang, B. Feng, Y. Qiu, Z. Gongque, K. He, Z. Wang, et al · 2023
Cited alongside, same era.
Self-explanation prompting improves dialogue understanding in large language models
H. Gao, T.-E. Lin, H. Li, M. Yang, Y. Wu, W. Ma, and Y. Li · 2023
Cited alongside, same era.
K. Shum, S. Diao, and T. Zhang · 2023
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Large language models in medicine
A. J. Thirunavukarasu, D. S. J. Ting, K. Elangovan, L. Gutierrez, T. F. Tan, and D. S. W. Ting · 2023
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Ethical implications of large language models a multidimensional exploration of societal, economic, and technical concerns
K.-J. Tokayev · 2023
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Chatgpt: five priorities for research
E. A. Van Dis, J. Bollen, W. Zuidema, R. van Rooij, and C. L. Bockting · 2023
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Metacognitive prompting improves understanding in large language models
Y. Wang and Y. Zhao · 2023
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Leveraging large language models to power chatbots for collecting user self-reported data
J. Wei, S. Kim, H. Jung, and Y.-H. Kim · 2023
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A prompt pattern catalog to enhance prompt engineering with ChatGPT
J. White, Q. Fu, S. Hays, M. Sandborn, C. Olea, H. Gilbert, A. Elnashar, J. Spencer-Smith, and D. C. Schmidt · 2023
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Fundamental limitations of alignment in large language models
Y. Wolf, N. Wies, Y. Levine, and A. Shashua · 2023
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BloombergGPT: A large language model for finance
S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, and G. Mann · 2023
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Decomposition enhances reasoning via self-evaluation guided decoding
Y. Xie, K. Kawaguchi, Y. Zhao, X. Zhao, M.-Y. Kan, J. He, and Q. Xie · 2023
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An LLM can fool itself: A prompt-based adversarial attack
X. Xu, K. Kong, N. Liu, L. Cui, D. Wang, J. Zhang, and M. Kankanhalli · 2023
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RCOT: Detecting and rectifying factual inconsistency in reasoning by reversing chain-of-thought
T. Xue, Z. Wang, Z. Wang, C. Han, P. Yu, and H. Ji · 2023
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Tree of thoughts: Deliberate problem solving with large language models
S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan · 2023
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Towards better chain-of-thought prompting strategies: A survey
Z. Yu, L. He, Z. Wu, X. Dai, and J. Chen · 2023
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A. Zafar, V. B. Parthasarathy, C. L. Van, S. Shahid, A. Shahid, et al · 2023
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Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts
J. Zamfirescu-Pereira, R. Y. Wong, B. Hartmann, and Q. Yang · 2023
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A survey of large language models
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al · 2023
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Progressive-hint prompting improves reasoning in large language models
C. Zheng, Z. Liu, E. Xie, Z. Li, and Y. Li · 2023
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A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
C. Zhou, Q. Li, C. Li, J. Yu, Y. Liu, G. Wang, K. Zhang, C. Ji, Q. Yan, L. He, et al · 2023
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Exploring ai ethics of chatgpt: A diagnostic analysis
T. Y. Zhuo, Y. Huang, C. Chen, and Z. Xing · 2023
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