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CSLLM: Code-Specific Large Language Models—A Survey
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AI
DOI：10.1016/j.eswa.2025.130991.png)
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摘要
En 中文
• CSLLMs tackle syntax, efficiency, and security gaps in general LLMs. • Review compares Codex, CodeT5, StarCoder, and DeepSeekCoder models. • Identifier-aware pretraining and RAG enhance CSLLM performance. • CSLLMs show higher accuracy, syntax correctness, and reliability. • Ethical risks, bias, and high complexity remain open research issues.
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
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暂无机构信息
引用论文
Nazi, Z.A.; Hossain, M.R.; Mamun, F.A. Evaluation of open and closed-source LLMs for low-resource language with zero-shot, few-shot, and chain-of-thought prompting. Nat. Lang. Process. J. 2025, 10, 100124. [Google Scholar] [CrossRef]Nazi, Z.A.; Hossain, M.R.; Mamun, F.A. 对低资源语言中开源和闭源LLMs的评估，采用零样本、少样本和思维链提示方法。Nat. Lang. Process. J. 2025, 10, 100124. [Google Scholar] [CrossRef].png)
IF0
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分享收藏Zhang, Z.; Yao, Y.; Zhang, A.; Tang, X.; Ma, X.; He, Z.; Wang, Y.; Gerstein, M.; Wang, R.; Liu, G.; et al. Igniting language intelligence: The hitchhiker’s guide from chain-of-thought reasoning to language agents. ACM Comput. Surv. 2025, 57, 1–39. [Google Scholar] [CrossRef]张, Z.; 姚, Y.; 张, A.; 汤, X.; 马, X.; 何, Z.; 王, Y.; Gerstein, M.; 王, R.; 刘, G.; et al. 点燃语言智能: 从思维链推理到语言代理的 hitchhiker’s guide. ACM Comput. Surv. 2025, 57, 1–39. [Google Scholar] [CrossRef]
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分享收藏Chen, B.; Zhang, Z.; Langrené, N.; Zhu, S. Unleashing the Potential of Prompt Engineering for Large Language Models. Patterns 2025, 6, 101260. [Google Scholar] [CrossRef] [PubMed]陈, B.; 张, Z.; 兰格伦, N.; 朱, S. 深挖提示工程的潜力以用于大型语言模型. 模式 2025, 6, 101260. [谷歌学术] [交叉引用] [PubMed]
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