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Large language models (LLMs) excel on new tasks without additional training, simply by providing natural language prompts that demonstrate how the task should be performed.
Leveraging BERT for extractive text summarization on lectures
Miller, D. (2019) · 1906
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Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., and Brew, J. (2019) · 1910
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A value for n-person games
Shapley, L. S. (1953) · 1953
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On the complexity of cooperative solution concepts
Deng, X. and Papadimitriou, C. H. (1994) · 1994
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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) · 2010
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C. (2013) · 2013
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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) · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S. (2017) · 2017
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Data shapley: Equitable valuation of data for machine learning
Ghorbani, A. and Zou, J. Y. (2019) · 2019
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Visualizing and understanding the effectiveness of BERT
Hao, Y., Dong, L., Wei, F., and Xu, K. (2019) · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C. and Toutanova, L. K. (2019) · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I. (2019) · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R. (2019) · 2019
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020) · 2020
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How can we know what language models know
Jiang, Z., Xu, F. F., Araki, J., and Neubig, G. (2020) · 2020
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Shapley values for feature selection: The good, the bad, and the axioms
Fryer, D. V., Strümke, I., and Nguyen, H. D. (2021) · 2021
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Making pre-trained language models better few-shot learners
Gao, T., Fisch, A., and Chen, D. (2021) · 2021
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WARP: word-level adversarial reprogramming
Hambardzumyan, K., Khachatrian, H., and May, J. (2021) · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N. (2021) · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P. (2021) · 2021
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Dealer: An end-to-end model marketplace with differential privacy
Liu, J., Lou, J., Liu, J., Xiong, L., Pei, J., and Sun, J. (2021) · 2021
Cited alongside, same era.
Evaluating gender bias in natural language inference
Sharma, S., Dey, M., and Sinha, K. (2021) · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Zhao, Z., Wallace, E., Feng, S., Klein, D., and Singh, S. (2021) · 2021
Cited alongside, same era.
Factual probing is [MASK]: learning vs. learning to recall
Zhong, Z., Friedman, D., and Chen, D. (2021) · 2021
Cited alongside, same era.
PADA: example-based prompt learning for on-the-fly adaptation to unseen domains
Active prompting with chain-of-thought for large language models
Diao, S., Wang, P., Lin, Y., and Zhang, T. (2023) · 2023
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Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H., Sabharwal, A., Clark, P., and Khot, T. (2023b) · 2023
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Mathprompter: Mathematical reasoning using large language models
Imani, S., Du, L., and Shrivastava, H. (2023) · 2023
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Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K. (2023) · 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) · 2023
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Ben-David, E., Oved, N., and Reichart, R. (2022) · 2022
Cited alongside, same era.
On the advance of making language models better reasoners
Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J., and Chen, W. (2022) · 2022
Cited alongside, same era.
Holistic evaluation of language models
Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., Newman, B., Yuan, B., Yan, B., Zhang, C., Cosgrove, C., Manning, C. D., Ré, C., Acosta-Navas, D., Hudson, D. A., Zelikman, E., Durmus, E., Ladhak, F., Rong, F., Ren, H., Yao, H., Wang, J., Santhanam, K., Orr, L. J., Zheng, L., Yüksekgönül, M., Suzgun, M., Kim, N., Guha, N., Chatterji, N. S., Khattab, O., Henderson, P., Huang, Q., Chi, R., Xie, S. M., Santurkar, S., Ganguli, S., Hashimoto, T., Icard, T., Zhang, T., Chaudhary, V., Wang, W., Li, X., Mai, Y., Zhang, Y., and Koreeda, Y. (2022) · 2022
Cited alongside, same era.
Learning to retrieve prompts for in-context learning
Rubin, O., Herzig, J., and Berant, J. (2022) · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., Kluska, A., Lewkowycz, A., Agarwal, A., Power, A., Ray, A., Warstadt, A., Kocurek, A. W., Safaya, A., Tazarv, A., Xiang, A., Parrish, A., Nie, A., Hussain, A., Askell, A., Dsouza, A., Rahane, A., Iyer, A. S., Andreassen, A., Santilli, A., Stuhlmüller, A., Dai, A. M., La, A., Lampinen, A. K., Zou, A., Jiang, A., Chen, A., Vuong, A., Gupta, A., Gottardi, A., Norelli, A., Venkatesh, A., Gholamidavoodi, A., Tabassum, A., Menezes, A., Kirubarajan, A., Mullokandov, A., Sabharwal, A., Herrick, A., Efrat, A., Erdem, A., Karakas, A., and et al. (2022) · 2022
Cited alongside, same era.
Lamda: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · 2022
Cited alongside, same era.
Rationale-augmented ensembles in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., and Zhou, D. (2022) · 2022
Cited alongside, same era.
OpenAI (2023) · 2023
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Selective annotation makes language models better few-shot learners
Su, H., Kasai, J., Wu, C. H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N. A., and Yu, T. (2023) · 2023
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., Chowdhery, A., and Zhou, D. (2023) · 2023
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Improving probability-based prompt selection through unified evaluation and analysis
Yang, S., Kim, J., Jang, J., Ye, S., Lee, H., and Seo, M. (2023) · 2023
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Dynamic shapley value computation
Zhang, J., Xia, H., Sun, Q., Liu, J., Xiong, L., Pei, J., and Ren, K. (2023b) · 2023
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Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A. (2023c) · 2023
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Chatbot arena: An open platform for evaluating llms by human preference
Chiang, W., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., Zhang, H., Zhu, B., Jordan, M. I., Gonzalez, J. E., and Stoica, I. (2024) · 2024
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PromptAI. https://prompti.ai/
PromptAI (2024) · 2024
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PromptBase. https://promptbase.com/
PromptBase (2024) · 2024
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Promptrr.io. https://promptrr.io/
Promptrr.io (2024) · 2024
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PromptWink. https://www.promptwink.com/
PromptWink (2024) · 2024
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Rethinking data shapley for data selection tasks: Misleads and merits
Wang, J. T., Yang, T., Zou, J., Kwon, Y., and Jia, R. (2024) · 2024
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Xu, Z., Peng, K., Ding, L., Tao, D., and Lu, X. (2024) · 2024
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