Development of a Question Answering System Over Building Codes using Retrieval Augmented Generation
Bibliographic record
Abstract
Building codes establish standards for the design, construction, and safety of buildings, ensuring structural integrity, fire protection, and accessibility. However, they are extensive, complex, and frequently updated, making manual querying time-consuming. A promising solution is a Question Answering (QA) system built on Retrieval Augmented Generation (RAG) or its extension, Multi-modal RAG (MRAG). RAG integrates a retriever with a Large Language Model (LLM), while MRAG employs Vision Language Models (VLMs) capable of processing both text and images. This study first evaluated retrieval methods for the National Building Code of Canada (NBCC), comparing pre-trained and fine-tuned LLMs. Results showed Elasticsearch to be the most effective retriever, while fine-tuning LLMs on NBCC data significantly improved domain-specific response generation. Since RAG struggles with tabular data, MRAG was explored as a means to incorporate tabular information. Different input formats, LaTeX and images of tables, were tested, with image-based inputs performing better, though still limited. To address this, Low Rank Adaptation (LoRA) was applied for fine-tuning VLMs on tabular NBCC datasets. Fine-tuned VLMs, particularly Qwen2.5-VL-3B, showed marked improvements, recording a relative 105 percent performance gain. Finally, fine-tuned VLMs were integrated into MRAG and evaluated on a manually prepared NBCC QA dataset that included both text and tables. In addition to these, MRAG framework based on commercial VLMs were also tested on the same dataset, resulting in multiple MRAG frameworks built with either open-source fine-tuned or commercial VLMs. The effectiveness of each end-to-end MRAG framework was then assessed using a combined metric that considered accuracy, ROUGE score, BERT score, and time. Among open-source models, the fine-tuned Qwen2.5 series achieved the best performance, benefiting from their strong reasoning and visual question answering capabilities. Among commercial models, Gemini-2-Flash and Gemini-2.5-Pro delivered the highest overall scores, 0.69 and 0.67 respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".