mlsanityData to 14 Oct 2026

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

arXiv 2609.18437 ↗First posted 16 Sep 2026cs.IRcited by 0

On the map: Retrieval-augmented generation → Medical and biomedical RAG

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.

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.

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13 of 32 references.

References · 13

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  4. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 281
  5. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
  6. 2022POTATO: The Portable Text Annotation Toolcited by 64
  7. 2023HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Sciencecited by 22
  8. 2023PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literaturecited by 13
  9. 2025Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Sciencecited by 9
  10. 2025Around the World in 24 Hours: Probing LLM Knowledge of Time and Placecited by 7
  11. 2024MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Sciencecited by 5
  12. 2023Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domaincited by 2
1 more
  1. 2025Incorporating Domain Knowledge into Materials Tokenizationcited by 2

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  1. 2024Adopting RAG for LLM-Aided Future Vehicle Design
  2. 2025Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive HIL Testing
  3. 2025Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety
  4. 2026From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals
  5. 2026FAB-Bench: A Framework for Adaptive RAG Benchmarking in Semiconductor Manufacturing
  6. 2023Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
  7. 2024AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing
  8. 2026Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152