Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning-Reference-Cited by-同舟云学术

Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Published:2024-02-06 Issue: Volume:32 Page:1-13
ISSN:
Container-title:Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
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Author:

Geng Mingyang¹^ORCID,Wang Shangwen¹^ORCID,Dong Dezun¹^ORCID,Wang Haotian¹^ORCID,Li Ge²^ORCID,Jin Zhi²^ORCID,Mao Xiaoguang¹^ORCID,Liao Xiangke¹^ORCID

Affiliation:

1. National University of Defense Technology, Changsha, China

2. Peking University, Beijing, China

Funder

National Key Research and Development Program Project Heterogeneous Computing Fusion of Cross-Domain Resources'

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3608134

Reference78 articles.

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3. Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020. A transformer-based approach for source code summarization. arXiv preprint arXiv:2005.00653 (2020).

4. Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2018. code2seq: Generating sequences from structured representations of code. arXiv preprint arXiv:1808.01400 (2018).

5. Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2019. code2seq: Generating Sequences from Structured Representations of Code. In Proceedings of the 7th International Conference on Learning Representations. OpenReview.net.

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