R. N. D. Guruge
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
R. N. D. Guruge has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Ali Darejeh
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
1 shared publication - Balachandra Arachchige Isuru Eranga
Univ. of Moratuwa
1 shared publication - Himal Suranga Jayasena
Department of Building Economics, University of Moratuwa, Sri Lanka
1 shared publication - P. V. I. N. Saparamadu
Department of Building Economics, Faculty of Architecture, University of Moratuwa, Moratuwa 10400, Sri Lanka
1 shared publication - Saleh Mohammad Ebrahimzadeh Sepasgozar
School of Built Environment, University of New South Wales
1 shared publication - Sanee Mohammad Ebrahimzadeh
Department of Media Management and Business Communications, Faculty of Business, College of Management, University of Tehran, Tehran 1417466191, Iran
1 shared publication
Latest Publications
- Open Access
Optimising Contract Interpretations with Large Language Models: A Comparative Evaluation of a Vector Database-Powered Chatbot vs. ChatGPT
Frequent ambiguities in contract terms often lead to costly legal disputes and project delays in the construction industry. Large Language Models (LLMs) offer a promising solution, enhancing accuracy and reducing misinterpretations. As studies pointed out, many professionals use LLMs, such as ChatGPT, to assist with their professional tasks at a minor level, such as information retrieval from the Internet and content editing. With access to a construction regulation database, LLMs can automate contract interpretation. However, the lack of Artificial Intelligence tools tailored to industry regulations hinders their adoption in the construction sector. This research addresses the gap by developing and deploying a publicly available specialised chatbot using the ChatGPT language model. The development process includes architectural design, data preparation, vector embeddings, and model integration. The study uses qualitative and quantitative methodologies to evaluate the chatbot’s role in resolving contract-related issues through standardised tests. The specialised chatbot, trained on construction-specific legal information, achieved an average score of 88%, significantly outperforming ChatGPT’s 36%. The integration of a domain-specific language model promises to revolutionise construction practices through increased precision, efficiency, and innovation. These findings demonstrate the potential of optimised language models to transform construction practices.
Open AccessOptimising Contract Interpretations with Large Language Models: A Comparative Evaluation of a Vector Database-Powered Chatbot vs. ChatGPT
Frequent ambiguities in contract terms often lead to costly legal disputes and project delays in the construction industry. Large Language Models (LLMs) offer a promising solution, enhancing accuracy and reducing misinterpretations. As studies pointed out, many professionals use LLMs, such as ChatGPT, to assist with their professional tasks at a minor level, such as information retrieval from the Internet and content editing. With access to a construction regulation database, LLMs can automate contract interpretation. However, the lack of Artificial Intelligence tools tailored to industry regulations hinders their adoption in the construction sector. This research addresses the gap by developing and deploying a publicly available specialised chatbot using the ChatGPT language model. The development process includes architectural design, data preparation, vector embeddings, and model integration. The study uses qualitative and quantitative methodologies to evaluate the chatbot’s role in resolving contract-related issues through standardised tests. The specialised chatbot, trained on construction-specific legal information, achieved an average score of 88%, significantly outperforming ChatGPT’s 36%. The integration of a domain-specific language model promises to revolutionise construction practices through increased precision, efficiency, and innovation. These findings demonstrate the potential of optimised language models to transform construction practices.