MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval
Summary: arXiv:2604.09552v1 Announce Type: cross
Abstract: Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG) systems. Building upon the DesignQA framework, which relied on full-text ingestion and text-based retrieval, this work establishes a Multimodal ColPali Enhanced Retrieval and Reasoning Framework (MCERF), a system that couples a multimodal retriever with large language model reasoning for accurate and efficient question answering from engineering documents.
The MCERF system employs the ColPali, which retrieves both textual and visual information, and implements multiple retrieval and reasoning strategies:
- Hybrid Lookup mode: This mode is designed for explicit rule mentions, allowing users to directly query specific engineering rules.
- Vision to Text fusion: This strategy guides queries based on figures and tables, enhancing the understanding of visual data within engineering contexts.
- High Reasoning LLM mode: This mode tackles complex multimodal questions, ensuring that intricate queries are processed with high accuracy.
- SelfConsistency decision: This feature stabilizes responses, providing reliability in the answers generated by the system.
The modular framework design of MCERF offers a reusable template for future multimodal systems, regardless of the underlying model architecture. This flexibility is critical in the rapidly evolving field of artificial intelligence and multimodal processing.
Furthermore, this work establishes and compares two routing approaches: a single case routing approach and a multi-agent system. Both strategies dynamically allocate queries to optimal pipelines, ensuring efficiency and effectiveness in processing user inquiries.
Evaluation on the DesignQA benchmark illustrates that the MCERF system significantly enhances performance, improving average accuracy across all tasks with a relative gain of +41.1% from baseline RAG best results. This remarkable improvement showcases the system’s potential in multimodal and reasoning-intensive tasks, all without the need for complete rulebook ingestion.
The findings underline how vision-language retrieval, modular reasoning, and adaptive routing can facilitate scalable document comprehension in engineering use cases. As industries increasingly rely on complex engineering documents filled with diverse information types, the implementation of such advanced systems becomes crucial.
In summary, the MCERF framework stands as a pioneering effort in the integration of multimodal information retrieval and reasoning, offering a promising avenue for future research and application in engineering documentation and beyond.