Large Language Models for Multilingual Code Intelligence: A Survey
Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Python, with weaker performance in languages like Rust and OCaml. Since real-world systems are inherently polyglot, robust multilingual code intelligence is crucial. This survey focuses on two key tasks: multilingual code generation from shared natural-language requirements, and multilingual code translation that preserves semantics across languages. It reviews representative methods, benchmarks, and evaluation metrics, and highlights challenges and opportunities for trustworthy cross-language generalization.
| Authors | Chao Jiang, Dugang Liu, Cheng Wen et al. |
|---|---|
| Primary category | cs.SE |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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