Towards Automation in Cost Estimation: LLM-based Methodology for Classifying and Extracting Cost Data from Tender Documents

Abstract: Cost estimation in building industry largely relies on manually extracting and classifying textual descriptions, a process susceptible to human error. Although recent advancements in Large Language Models (LLMs) hold promise, their application in this domain requires further investigation.This study proposes a methodology to optimize LLM performance validated through the development of a tool that classifies cost descriptions into a three-level hierarchical taxonomy and extracts relevant information organising the data in a database as output. Results demonstrate a F1 score of 0.96 on classification tasks contributing to cost estimation automation, reducing manual processing, and enhancing knowledge management within the domain.