Exploring LLMs and RAG for Plausible and Explainable Material Prediction of Vehicle Components

2026-09-16

AI Summary

研究使用LLM和RAG方法预测车辆组件材料，通过对比生成模型、单次检索增强生成及迭代验证链方法，以提高预测准确性和解释性。

Abstract

In this work, we explore whether LLMs can accurately predict and explain plausible materials for vehicle components such as brake discs or fuel injectors without requiring extensive fine-tuning. We test and evaluate three approaches: a standard generative LLM baseline, a single-pass Retrieval-Augmented Generation (RAG) approach, and an iterative Chain-of-Verification (CoVe) variant. For retrieval, we rely on publicly available data using a domain-filtered Wikipedia corpus. Since no gold standard exists for this task, we develop a custom web-based annotation tool supporting crucial functions for structured domain expert evaluation. LLM-based generation substantially outperforms prior work, which is not further surpassed by the tested RAG approaches. Our results surface remaining challenges for RAG-based systems: hyperparameter optimization, the availability of high-quality, legally accessible domain corpora, and expert evaluation study design.

Problem

• LLMs
• material prediction
• vehicle components
• plausible materials

Innovation

• LLMs
• RAG
• CoVe
• Material Prediction
• Vehicle Components