利用大型语言模型实现制造业知识共享：用户评估与模型基准测试
Knowledge Sharing in Manufacturing using Large Language Models: User Evaluation and Model Benchmarking
AI总结：
本文提出基于LLM的工厂知识检索与共享系统，通过用户研究验证其提升信息检索和问题解决效率的潜力，并基准测试表明GPT-4最优而开源模型因隐私与定制优势具有吸引力。
摘要：
自然语言处理的最新进展为工厂中的知识共享提供了更智能的支持方式。在制造业中，操作生产线已变得越来越知识密集，这给工厂培训和支持新操作员的能力带来了压力。本文介绍了一个基于大型语言模型（LLM）的系统，旨在从工厂文档中包含的大量知识以及专家操作员共享的知识中检索信息。该系统旨在高效回答操作员的查询并促进新知识的共享。我们在一家工厂进行了用户研究，以评估其潜在影响和采用情况，得出了若干感知到的益处，即能够更快地检索信息和更高效地解决问题。然而，该研究也突显了当有人类专家可选时，操作员更倾向于向人类专家学习。此外，我们为该系统中多个商业和开源LLM进行了基准测试。当前最先进的模型GPT-4始终优于其他模型，而开源模型紧随其后，鉴于其在数据隐私和定制化方面的优势，开源模型是一个有吸引力的选择。总之，这项工作为考虑使用LLM工具进行知识管理的工厂提供了初步见解和系统设计。
英文摘要：
Recent advances in natural language processing enable more intelligent ways to support knowledge sharing in factories. In manufacturing, operating production lines has become increasingly knowledge-intensive, putting strain on a factory's capacity to train and support new operators. This paper introduces a Large Language Model (LLM)-based system designed to retrieve information from the extensive knowledge contained in factory documentation and knowledge shared by expert operators. The system aims to efficiently answer queries from operators and facilitate the sharing of new knowledge. We conducted a user study at a factory to assess its potential impact and adoption, eliciting several perceived benefits, namely, enabling quicker information retrieval and more efficient resolution of issues. However, the study also highlighted a preference for learning from a human expert when such an option is available. Furthermore, we benchmarked several commercial and open-sourced LLMs for this system. The current state-of-the-art model, GPT-4, consistently outperformed its counterparts, with open-source models trailing closely, presenting an attractive option given their data privacy and customization benefits. In summary, this work offers preliminary insights and a system design for factories considering using LLM tools for knowledge management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
