OpenAI：GPT-6 大幅提升老旧语言迁移准确率
OpenAI 发布研究称，其最新模型在 COBOL、Fortran、Visual Basic 等低资源编程语言上的软件构建与迁移能力明显提升；这些语言可用的训练代码和文档远少于 Python、JavaScript，却支撑着大量关键基础设施。在要求模型用 20 种语言从零构建兼容 SQLite 数据库引擎的 MultiLangBench 上，OpenAI 称 GPT-6 Astra 在最高推理强度下平均测试通过率为 87.4%，上一代 GPT-5.6 Sol 为 26.8%，其中 COBOL 从 10.9% 升至 72.1%，成本更低的 GPT-6 Luna 平均为 40.3%。在另一项将 COBOL 迁移至 C++、Java、Kotlin、Python 或 Rust 的评测中，OpenAI 称 GPT-6.1 Sol 低推理强度下准确率为 86.6%，GPT-5.6 Sol 为 5.4%，提高推理强度后达到 97.9%。OpenAI 同时提醒，将这类迁移结果用于生产软件仍需专家审查，并针对业务规则与系统依赖进行测试。
OpenAI 称 COBOL 支撑着超过 40% 的网上银行系统、80% 的线下信用卡交易和 95% 的 ATM 交易，因此模型对这些语言的支持改善，可能帮助团队维护和现代化关键服务所依赖的软件。
Our latest model advances bring stronger capabilities for building and migrating software in low-resource programming languages such as COBOL, Fortran, and Visual Basic. These languages have less code and documentation available for models to learn from than languages like Python and JavaScript, yet support critical infrastructure and essential services around the world. COBOL supports(opens in a new window) more than 40% of online banking systems, 80% of in-person credit card transactions and 95% of ATM transactions, while Fortran is used in weather forecasting(opens in a new window) and nuclear reactor safety analysis(opens in a new window). Improving AI’s ability to work in these languages could help teams maintain and modernize the software that essential services depend on.
This work is guided by feedback from developers and enterprise customers who have told us that coding models often struggle with these languages. Maintaining and modernizing this software can be difficult: documentation may be incomplete, original developers may have left the company, and important business rules may only exist in code. Whether teams are fixing bugs or migrating to another language, they need to preserve the behavior their systems depend on.
#OpenAI#GPT-6 Astra#GPT-6.1 Sol#GPT-6 Luna#GPT-5.6 Sol#COBOL#Fortran#Visual Basic#MultiLangBench#SQLite