Analysis of Language-Model-Powered Chatbots for Query Resolution in PDF-Based Automotive Manuals

IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium Pub Date : 2023-10-16 DOI:10.3390/vehicles5040076

Thaís Medeiros, Morsinaldo Medeiros, Mariana Azevedo, Marianne Silva, Ivanovitch Silva, Daniel G. Costa

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Abstract

In the current scenario of fast technological advancement, increasingly characterized by widespread adoption of Artificial Intelligence (AI)-driven tools, the significance of autonomous systems like chatbots has been highlighted. Such systems, which are proficient in addressing queries based on PDF files, hold the potential to revolutionize customer support and post-sales services in the automotive sector, resulting in time and resource optimization. Within this scenario, this work explores the adoption of Large Language Models (LLMs) to create AI-assisted tools for the automotive sector, assuming three distinct methods for comparative analysis. For them, broad assessment criteria are considered in order to encompass response accuracy, cost, and user experience. The achieved results demonstrate that the choice of the most adequate method in this context hinges on the selected criteria, with different practical implications. Therefore, this work provides insights into the effectiveness and applicability of chatbots in the automotive industry, particularly when interfacing with automotive manuals, facilitating the implementation of productive generative AI strategies that meet the demands of the sector.

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基于pdf的汽车手册查询解析的语言模型驱动聊天机器人分析

在当前技术快速发展的情况下，人工智能(AI)驱动工具的广泛采用日益成为特征，像聊天机器人这样的自主系统的重要性已经得到强调。这样的系统可以熟练地处理基于PDF文件的查询，有可能彻底改变汽车行业的客户支持和售后服务，从而优化时间和资源。在这种情况下，本工作探讨了采用大型语言模型(llm)为汽车行业创建人工智能辅助工具，并假设了三种不同的方法进行比较分析。对于他们来说，为了包含响应准确性、成本和用户体验，需要考虑广泛的评估标准。所取得的结果表明，在这种情况下选择最适当的方法取决于所选择的标准，具有不同的实际含义。因此，这项工作提供了对聊天机器人在汽车行业的有效性和适用性的见解，特别是在与汽车手册交互时，促进了满足该行业需求的生产性生成人工智能策略的实施。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium

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