The emergence of large language models (LLM), like GPT, is revolutionizing the field of information retrieval, finding applications across a wide range of domains. However, the intricate domain knowledge and the unique software paradigms inherent to the manufacturing sector have posed significant barriers to the effective utilization of LLM. To address this divide, an error-assisted fine-tuning approach is proposed to adapt LLM specifically for the manufacturing domain. Initially, the LLM is fine-tuned using a manufacturing-domain corpus, allowing it to learn and adapt to the nuances of the manufacturing field. Additionally, the injection of a labeled dataset into a pre-configured LLM enhances its ability to identify key elements within the domain. To ensure the generation of syntactically valid programs in domain-specific languages, and to accommodate environmental constraints, an error-assisted iterative prompting procedure is introduced, which facilitates the generation of reliable and expected code. Experimental results demonstrate the model’s proficiency in accurately responding to manufacturing-related queries and its effectiveness in generating reliable code, where the accuracy of judgment querying can experience an improvement of approximately 4.1%. By expanding the applicability of LLM to the manufacturing industry, it is hoped that this research will pave the way for a broad array of new LLM-based applications within manufacturing.

中文翻译：

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利用错误辅助微调大型语言模型实现卓越制造

像 GPT 这样的大型语言模型 (LLM) 的出现正在彻底改变信息检索领域，在广泛的领域中找到应用。然而，制造业固有的复杂领域知识和独特的软件范式对法学硕士的有效利用构成了重大障碍。为了解决这一分歧，提出了一种误差辅助微调方法，使法学硕士专门适用于制造领域。最初，法学硕士使用制造领域语料库进行微调，使其能够学习并适应制造领域的细微差别。此外，将标记数据集注入预先配置的法学硕士可以增强其识别领域内关键元素的能力。为了确保以特定领域语言生成语法上有效的程序，并适应环境限制，引入了错误辅助迭代提示过程，这有助于生成可靠且预期的代码。实验结果表明，该模型能够准确响应制造相关查询，并有效生成可靠的代码，判断查询的准确性可提高约4.1%。通过扩大法学硕士在制造业的适用性，希望这项研究能为制造业中基于法学硕士的广泛新应用铺平道路。