The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Open accessAbstract
Pre-trained models for programming languages have recently demonstrated great success on code intelligence.To support both code-related understanding and generation tasks, recent works attempt to pre-train unified encoder-decoder models.However, such encoder-decoder framework is sub-optimal for auto-regressive tasks, especially code completion that requires a decoder-only manner for efficient inference.In this paper, we present UniXcoder, a unified cross-modal pre-trained model for programming language.The model utilizes mask attention matrices with prefix adapters to control the behavior of the model and leverages cross-modal contents like AST and code comment to enhance code representation.To encode AST that is represented as a tree in parallel, we propose a one-to-one mapping method to transform AST in a sequence structure that retains all structural information from the tree.Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task.We evaluate UniXcoder on five code-related tasks over nine datasets.To further evaluate the performance of code fragment representation, we also construct a dataset for a new task, called zero-shot code-to-code search.Results show that our model achieves state-of-the-art performance on most tasks and analysis reveals that comment and AST can both enhance UniXcoder.
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Research works printed but not linked: 2
- Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, LIU Shujie, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al. 2020. Graphcode- bert: Pre-training code representations with data flow. In International Conference on Learning Rep- resentations.
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Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023) · Future Internet · doi:10.3390/fi15080260 · Cited by 638
A Survey on Large Language Models for Code Generation
Jiang, J., Wang, F., Shen, J., Kim, S., & Kim, S. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3747588 · Cited by 251
Automating code review activities by large-scale pre-training
Li, Z., Lu, S., Guo, D., Duan, N., Jannu, S., Jenks, G., Majumder, D., Green, J., Svyatkovskiy, A., Fu, S., et al. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549081 · Cited by 301
Self-citationRetrieval-Based Prompt Selection for Code-Related Few-Shot Learning
Nashid, N., Sintaha, M., & Mesbah, A. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00205 · Cited by 178
Self-Planning Code Generation with Large Language Models
Jiang, X., Dong, Y., Wang, L., Fang, Z., Shang, Q., Li, G., Jin, Z., & Jiao, W. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3672456 · Cited by 158
Metadata
- Authors
- Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., & Yin, J.
- Affiliations
- Sun Yat-sen University (CN)
- Microsoft Research Asia (China) (CN)
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 7212-7225
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4221166942
- Cited by
- 673
- References
- 28
- Citations by year
- 2026: 153 · 2025: 239 · 2024: 148 · 2023: 122 · 2022: 11
- FWCI
- 116.24
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (U1811262, U1811261, U2001211, U1811264, U1911203)
- Basic and Applied Basic Research Foundation of Guangdong Province (2019B15 15130001)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- UniXcoder, code representation, programming language models, code comments, contrastive learning, cross-modal generation, code completion, code intelligence
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