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Black-box prompt tuning employs derivative-free optimization algorithms to learn prompts within low-dimensional subspaces rather than back-propagating through the network of Large Language Models (LLMs).
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K. Papineni, S. Roukos, T. Ward, and W. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proc. of ACL , 2002, pp. 311–318. [Online]. Available: https://aclanthology.org/P02-1040
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N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES),” Evol. Comput. , vol. 11, no. 1, pp. 1–18, 2003. [Online]. Available: https://direct.mit.edu/evco/article-abstract/11/1/1/1139/Reducing-the-Time-Complexity-of-the-Derandomized
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C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Proc. of Text Summarization Branches Out , 2004, pp. 74–81. [Online]. Available: https://aclanthology.org/W04-1013
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B. Pang and L. Lee, “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,” in Proc. of ACL , 2005, pp. 115–124. [Online]. Available: https://aclanthology.org/P05-1015
2005
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S. Banerjee and A. Lavie, “METEOR: An automatic metric for MT evaluation with improved correlation with human judgments,” in Proc. of IEEvaluation@ACL , 2005, pp. 65–72. [Online]. Available: https://aclanthology.org/W05-0909
2005
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I. Dagan, O. Glickman, and B. Magnini, “The PASCAL recognising textual entailment challenge,” in Proc. of MLCW , 2005, pp. 177–190. [Online]. Available: https://link.springer.com/chapter/10.1007/11736790_9
2005
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W. B. Dolan and C. Brockett, “Automatically constructing a corpus of sentential paraphrases,” in Proc. of IWP@IJCNLP , 2005. [Online]. Available: https://aclanthology.org/I05-5002
2005
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R. Bar-Haim, I. Dagan, B. Dolan, L. Ferro, D. Giampiccolo, B. Magnini, and I. Szpektor, “The second PASCAL recognising textual entailment challenge,” in Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment , 2006, pp. 785–794. [Online]. Available: https://www.researchgate.net/publication/247999003_The_second_PASCAL_recognising_textual_entailment_challenge
2006
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D. Giampiccolo, B. Magnini, I. Dagan, and B. Dolan, “The third PASCAL recognizing textual entailment challenge,” in Proc. of ACL-PASCAL@ACL , 2007, pp. 1–9. [Online]. Available: https://aclanthology.org/W07-1401
2007
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A. R. Conn, K. Scheinberg, and L. N. Vicente, Introduction to derivative-free optimization . SIAM, 2009
2009
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L. Bentivogli, I. Dagan, H. T. Dang, D. Giampiccolo, and B. Magnini, “The fifth PASCAL recognizing textual entailment challenge,” vol. 7, no. 8, p. 1, 2009. [Online]. Available: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=9746994d09b5bf6c40bee3693ee8678e191f84b8
2009
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A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proc. of ACL , 2011, pp. 142–150. [Online]. Available: https://aclanthology.org/P11-1015
2011
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H. Gurulingappa, A. M. Rajput, A. Roberts, J. Fluck, M. Hofmann-Apitius, and L. Toldo, “Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports,” Journal of Biomedical Informatics , vol. 45, no. 5, pp. 885 – 892, 2012. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1532046412000615
2012
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R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proc. of EMNLP , 2013, pp. 1631–1642. [Online]. Available: https://aclanthology.org/D13-1170
2013
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D. Wierstra, T. Schaul, T. Glasmachers, Y. Sun, J. Peters, and J. Schmidhuber, “Natural evolution strategies,” J. Mach. Learn. Res. , vol. 15, no. 1, pp. 949–980, 2014. [Online]. Available: https://dl.acm.org/doi/10.5555/2627435.2638566
2014
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X. Zhang, J. J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” in Proc. of NeurIPS , 2015, pp. 649–657. [Online]. Available: https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html
2015
Cited alongside, same era.
2015
Cited alongside, same era.
S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning, “A large annotated corpus for learning natural language inference,” in Proc. of EMNLP . The Association for Computational Linguistics, 2015, pp. 632–642. [Online]. Available: https://aclanthology.org/D15-1075
2015
Cited alongside, same era.
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “SQuAD: 100, 000+ questions for machine comprehension of text,” in Proc. of EMNLP , 2016, pp. 2383–2392. [Online]. Available: https://aclanthology.org/D16-1264
T. Shin, Y. Razeghi, R. L. L. IV, E. Wallace, and S. Singh, “Autoprompt: Eliciting knowledge from language models with automatically generated prompts,” in Proc. of EMNLP , 2020, pp. 4222–4235. [Online]. Available: https://aclanthology.org/2020.emnlp-main.346
2020
Later among the works it cites.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush, “Transformers: State-of-the-art natural language processing,” in Proc. of EMNLP: System Demonstrations , 2020, pp. 38–45. [Online]. Available: https://aclanthology.org/2020.emnlp-demos.6
2020
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Z. Huang and T. Zhang, “Black-box adversarial attack with transferable model-based embedding,” in Proc. of ICLR , 2020. [Online]. Available: https://openreview.net/forum?id=SJxhNTNYwB
2020
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2016
Cited alongside, same era.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. of ICML , 2017, pp. 1126–1135. [Online]. Available: http://proceedings.mlr.press/v70/finn17a.html
2017
Cited alongside, same era.
C. Gardent, A. Shimorina, S. Narayan, and L. Perez-Beltrachini, “The WebNLG challenge: Generating text from RDF data,” in Proc. of INLG , 2017, pp. 124–133. [Online]. Available: https://aclanthology.org/W17-3518
2017
Cited alongside, same era.
J. Novikova, O. Dusek, and V. Rieser, “The E2E dataset: New challenges for end-to-end generation,” in Proc. of SIGdial , 2017, pp. 201–206. [Online]. Available: https://doi.org/10.18653/v1/w17-5525
2017
Cited alongside, same era.
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in Proc. of NeurIPS , 2018. [Online]. Available: https://proceedings.neurips.cc/paper/2018/hash/69386f6bb1dfed68692a24c8686939b9-Abstract.html
2018
Cited alongside, same era.
J. Gu, Y. Wang, Y. Chen, V. O. K. Li, and K. Cho, “Meta-learning for low-resource neural machine translation,” in Proc. of EMNLP , 2018, pp. 3622–3631. [Online]. Available: https://aclanthology.org/D18-1398
2018
Cited alongside, same era.
A. Williams, N. Nangia, and S. R. Bowman, “A broad-coverage challenge corpus for sentence understanding through inference,” in Proc. of NAACL , 2018, pp. 1112–1122. [Online]. Available: https://aclanthology.org/N18-1101
2018
Cited alongside, same era.
O. de Gibert, N. Pérez, A. G. Pablos, and M. Cuadros, “Hate speech dataset from a white supremacy forum,” in Proc. of ALW@EMNLP , 2018, pp. 11–20. [Online]. Available: https://aclanthology.org/W18-5102
2018
Cited alongside, same era.
J. Thorne, A. Vlachos, C. Christodoulopoulos, and A. Mittal, “FEVER: a large-scale dataset for fact extraction and verification,” in Proc. of NAACL , 2018, pp. 809–819. [Online]. Available: https://aclanthology.org/N18-1074
2018
Cited alongside, same era.
X. L. Li and P. Liang, “Prefix-Tuning: Optimizing continuous prompts for generation,” in Proc. of ACL , 2021, pp. 4582–4597. [Online]. Available: https://aclanthology.org/2021.acl-long.353
2021
Later among the works it cites.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” in Proc. of EMNLP , 2021, pp. 3045–3059. [Online]. Available: https://aclanthology.org/2021.emnlp-main.243
2021
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2021
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T. Schick and H. Schütze, “It’s not just size that matters: Small language models are also few-shot learners,” in Proc. of NAACL , 2021, pp. 2339–2352. [Online]. Available: https://aclanthology.org/2021.naacl-main.185
2021
Later among the works it cites.
T. Gao, A. Fisch, and D. Chen, “Making pre-trained language models better few-shot learners,” in Proc. of ACL , 2021, pp. 3816–3830
2021
Later among the works it cites.
2021
Later among the works it cites.
T. Sun, Y. Shao, H. Qian, X. Huang, and X. Qiu, “Black-box tuning for language-model-as-a-service,” in Proc. of ICML , 2022, pp. 20 841–20 855. [Online]. Available: https://proceedings.mlr.press/v162/sun22e.html
2022
Later among the works it cites.
T. Sun, Z. He, H. Qian, Y. Zhou, X. Huang, and X. Qiu, “BBTv2: Towards a gradient-free future with large language models,” in Proc. of EMNLP , 2022, pp. 3916–3930. [Online]. Available: https://aclanthology.org/2022.emnlp-main.259
2022
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T. Vu, B. Lester, N. Constant, R. Al-Rfou’, and D. Cer, “SPoT: Better frozen model adaptation through soft prompt transfer,” in Proc. of ACL , 2022, pp. 5039–5059. [Online]. Available: https://aclanthology.org/2022.acl-long.346
2022
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Y. Su, X. Wang, Y. Qin, C. Chan, Y. Lin, H. Wang, K. Wen, Z. Liu, P. Li, J. Li, L. Hou, M. Sun, and J. Zhou, “On transferability of prompt tuning for natural language processing,” in Proc. of NAACL , 2022, pp. 3949–3969. [Online]. Available: https://aclanthology.org/2022.naacl-main.290
2022
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Y. Hou, H. Dong, X. Wang, B. Li, and W. Che, “MetaPrompting: Learning to learn better prompts,” in Proc. of COLING , 2022, pp. 3251–3262. [Online]. Available: https://aclanthology.org/2022.coling-1.287
2022
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Y. Huang, K. Qian, and Z. Yu, “Learning a better initialization for soft prompts via meta-learning,” in Proc. of IJCNLP , 2023, pp. 67–75. [Online]. Available: https://aclanthology.org/2023.ijcnlp-short.8
2023
Closest in time.
2023
Closest in time.
S. Rosenthal, N. Farra, and P. Nakov, “SemEval-2017 task 4: Sentiment analysis in twitter,” in Proc. of SemEval , 2017, pp. 502–518. [Online]. Available: https://aclanthology.org/S17-2088
2088
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