Knowledge-Aware Code Generation with Large Language Models-Reference-Cited by-同舟云学术

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Knowledge-Aware Code Generation with Large Language Models

Published:2024-04-15 Issue: Volume: Page:52-63
ISSN:
Container-title:Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension
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Author:

Huang Tao¹^ORCID,Sun Zhihong¹^ORCID,Jin Zhi²^ORCID,Li Ge²^ORCID,Lyu Chen¹³^ORCID

Affiliation:

1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

2. Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education; School of Computer Science, Peking University, Beijing, China

3. School of Computer Science, Peking University, Beijing, China

Funder

Natural Science Foundation of Shandong Province, China

National Natural Science Foundation of China-Liaoning Joint Fund

National Key R&D Program

Key Program of Hubei

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3643916.3644418

Reference33 articles.

1. Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. Synchromesh: Reliable code generation from pretrained language models. In International Conference on Learning Representations, 2021.

2. Incorporating domain knowledge through task augmentation for front-end JavaScript code generation

3. Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. Codet: Code generation with generated tests. In The Eleventh International Conference on Learning Representations, 2022.

4. CODEP: Grammatical Seq2Seq Model for General-Purpose Code Generation

5. Embedding API dependency graph for neural code generation

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