Development of a Question Answering System Over Building Codes using Retrieval Augmented Generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it