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

arXiv: https://arxiv.org/abs/2609.18437 · HTML: https://mlsanity.com/paper/2609.18437/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 13 of 13)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [The Power of Noise: Redefining Retrieval for RAG Systems](https://mlsanity.com/paper/2401.14887/) (2024)
- [Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?](https://mlsanity.com/paper/2308.10168/) (2023)
- [POTATO: The Portable Text Annotation Tool](https://mlsanity.com/paper/2212.08620/) (2022)
- [HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science](https://mlsanity.com/paper/2310.08511/) (2023)
- [PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature](https://mlsanity.com/paper/2311.07715/) (2023)
- [Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science](https://mlsanity.com/paper/2506.04410/) (2025)
- [Around the World in 24 Hours: Probing LLM Knowledge of Time and Place](https://mlsanity.com/paper/2506.03984/) (2025)
- [MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science](https://mlsanity.com/paper/2410.15126/) (2024)
- [Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain](https://mlsanity.com/paper/2304.14745/) (2023)
- [Incorporating Domain Knowledge into Materials Tokenization](https://mlsanity.com/paper/2506.11115/) (2025)

## Abstract (arXiv)

In this work, we explore whether LLMs can predict and explain plausible materials for vehicle components such as brake discs or fuel injectors without extensive fine-tuning. We compare a standard generative LLM baseline, single-pass Retrieval-Augmented Generation (RAG), and an iterative Chain-of-Verification (CoVe) variant across two LLMs, using a domain-filtered Wikipedia corpus that proved helpful in prior work. As no gold standard exists, we develop and release a reusable web-based annotation tool supporting crucial functions for structured domain-expert judgments, such as highlighting, ratings and ranking. Expert agreement is reasonable for plausibility, lower for ratings. LLM-based generation compares favorably to prior work. RAG and CoVe do not improve on it and are partly rated worse by experts. Contrary to prior work, Wikipedia-based retrieval does not help in our setup.