Adapting LLMs for Efficient Context Processing through Soft Prompt Compression

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M. Gambhir and V. Gupta, “Recent automatic text summarization techniques: a survey,” Artificial Intelligence Review
2016
Earlier work this paper cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” 2019
2019
Earlier work this paper cites.
I. Beltagy, M. E. Peters, and A. Cohan, “Longformer: The long-document transformer,” 2020
2020
Earlier work this paper cites.
M. Zaheer, G. Guruganesh, A. Dubey, J. Ainslie, C. Alberti, S. Ontanon, P. Pham, A. Ravula, Q. Wang, L. Yang, and A. Ahmed, “Big bird: Transformers for longer sequences,” 2021
2021
Earlier work this paper cites.

X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” 2021
2021
Cited alongside, same era.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” 2021
2021
Cited alongside, same era.
Y. Su, X. Wang, Y. Qin, C.-M. 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 Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
2022
Cited alongside, same era.
J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de Las Casas, L. A. Hendricks, J. Welbl, A. Clark, et al
2022
Cited alongside, same era.

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Adapting LLMs for Efficient Context Processing through Soft Prompt Compression · Around

M. Gambhir and V. Gupta, “Recent automatic text summarization techniques: a survey,” Artificial Intelligence Review
2016
Earlier work this paper cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” 2019
2019
Earlier work this paper cites.
I. Beltagy, M. E. Peters, and A. Cohan, “Longformer: The long-document transformer,” 2020
2020
Earlier work this paper cites.
M. Zaheer, G. Guruganesh, A. Dubey, J. Ainslie, C. Alberti, S. Ontanon, P. Pham, A. Ravula, Q. Wang, L. Yang, and A. Ahmed, “Big bird: Transformers for longer sequences,” 2021
2021
Earlier work this paper cites.

X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” 2021
2021
Cited alongside, same era.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” 2021
2021
Cited alongside, same era.
Y. Su, X. Wang, Y. Qin, C.-M. 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 Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
2022
Cited alongside, same era.
J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de Las Casas, L. A. Hendricks, J. Welbl, A. Clark, et al
2022
Cited alongside, same era.

alphaXiv searches the wider corpus for related work and actual follow-ups.

alphaXiv is searching for related work…