Enhancing Building Semantics Preservation in AI Model Training with Large Language Model Encodings
arXiv:2602.15791v1 Announce Type: new Abstract: Accurate representation of building semantics, encompassing both generic object types and specific subtypes, is essential for effective AI model training in the architecture, engineering, construction, and operation (AECO) industry. Conventional encoding methods (e.g., one-hot) often fail to convey the nuanced relationships among closely related subtypes, limiting AI's semantic comprehension. To address this limitation, this study proposes a novel training approach that employs large language model (LLM) embeddings (e.g., OpenAI GPT and Meta LLaMA) as encodings to preserve finer distinctions in building semantics. We evaluated the proposed method by training GraphSAGE models to classify 42 building object subtypes across five high-rise residential building information models (BIMs). Various embedding dimensions were tested, including original high-dimensional LLM embeddings (1,536, 3,072, or 4,096) and 1,024-dimensional compacted embeddi
arXiv:2602.15791v1 Announce Type: new Abstract: Accurate representation of building semantics, encompassing both generic object types and specific subtypes, is essential for effective AI model training in the architecture, engineering, construction, and operation (AECO) industry. Conventional encoding methods (e.g., one-hot) often fail to convey the nuanced relationships among closely related subtypes, limiting AI's semantic comprehension. To address this limitation, this study proposes a novel training approach that employs large language model (LLM) embeddings (e.g., OpenAI GPT and Meta LLaMA) as encodings to preserve finer distinctions in building semantics. We evaluated the proposed method by training GraphSAGE models to classify 42 building object subtypes across five high-rise residential building information models (BIMs). Various embedding dimensions were tested, including original high-dimensional LLM embeddings (1,536, 3,072, or 4,096) and 1,024-dimensional compacted embeddings generated via the Matryoshka representation model. Experimental results demonstrated that LLM encodings outperformed the conventional one-hot baseline, with the llama-3 (compacted) embedding achieving a weighted average F1-score of 0.8766, compared to 0.8475 for one-hot encoding. The results underscore the promise of leveraging LLM-based encodings to enhance AI's ability to interpret complex, domain-specific building semantics. As the capabilities of LLMs and dimensionality reduction techniques continue to evolve, this approach holds considerable potential for broad application in semantic elaboration tasks throughout the AECO industry.
Executive Summary
This study proposes a novel approach to enhance building semantics preservation in AI model training by utilizing large language model (LLM) encodings. The results demonstrate that LLM encodings outperform conventional one-hot encoding, achieving a higher weighted average F1-score in classifying building object subtypes. The study highlights the potential of LLM-based encodings in improving AI's ability to interpret complex, domain-specific building semantics, with significant implications for the architecture, engineering, construction, and operation (AECO) industry.
Key Points
- ▸ LLM encodings can preserve finer distinctions in building semantics
- ▸ The proposed approach outperforms conventional one-hot encoding
- ▸ Compacted LLM embeddings can achieve comparable performance to high-dimensional embeddings
Merits
Improved Semantic Comprehension
The use of LLM encodings enables AI models to better understand nuanced relationships among building object subtypes, leading to more accurate classification and improved overall performance.
Demerits
Computational Complexity
The proposed approach may require significant computational resources, particularly when working with high-dimensional LLM embeddings, which could be a limitation for large-scale applications.
Expert Commentary
The study's results demonstrate the effectiveness of LLM encodings in preserving building semantics, which has significant implications for the AECO industry. The use of compacted LLM embeddings can help mitigate computational complexity, making the approach more feasible for large-scale applications. However, further research is needed to fully explore the potential of LLM-based encodings and to address the challenges associated with their adoption in practice. The study's findings also highlight the need for ongoing collaboration between academia, industry, and regulatory bodies to ensure the effective integration of LLM-based encodings in AECO applications.
Recommendations
- ✓ Further research on the application of LLM encodings in various AECO tasks and domains
- ✓ Development of more efficient and scalable methods for compacting LLM embeddings
