The real estate market relies heavily on structured data, such as property details, market trends, and price fluctuations. However, the lack of specialized Tabular Question Answering datasets in this domain limits the development of automated question-answering systems. To fill this gap, we introduce RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate. RETQA comprises 4,932 tables and 20,762 question-answer pairs across 16 sub-fields within three major domains: property information, real estate company finance information and land auction information. Compared with existing tabular question answering datasets, RETQA poses greater challenges due to three key factors: long-table structures, open-domain retrieval, and multi-domain queries. To tackle these challenges, we propose the SLUTQA framework, which integrates large language models with spoken language understanding tasks to enhance retrieval and answering accuracy. Extensive experiments demonstrate that SLUTQA significantly improves the performance of large language models on RETQA by in-context learning. RETQA and SLUTQA provide essential resources for advancing tabular question answering research in the real estate domain, addressing critical challenges in open-domain and long-table question-answering. The dataset and code are publicly available at \url{https://github.com/jensen-w/RETQA}.

A variety of numerical methods have sprung up later, including SIMP (Bendse, 1989; Zhou and Rozvany, 1991; Rozvany et al., 1992), evolutionary approaches(Xie and Steven, 1993), level-set method (Wang et al., 2003; Allaire et al., 2004), moving morphable components (Guo et al., 2014), and others. However, the computational cost is still one of the main hinders to widely introduce them into design practices, in particular for large structures (Sigmund and Maute, 2013). Withthe recent boost of machine learning algorithms andadvances in graphics processing units (GPU), machine learning (ML), especially the deep learning, which has been seen to make many successful stories in various fields, including automatic drive, image recognition, naturallanguage processing, and even art, may shed light on accelerating the adoption of topology optimization inmore design practices. Recently, a few attempts have been seen to apply ML on topology optimizations (Leiet al., 2018; Sosnovik and Oseledets, 2017; Banga et al., 2018; Yu et al., 2018). Theoretically, theoptimal layout of the material is a complicated function of the initial conditions based on the optimization objectiveand constraints. The neural network can implement approximating nonlinear functions by arbitrary accuracyas its depth increases. This characteristic makes it possible for the neural network to learn a target function which can directly give us the optimal structure without any iteration and effectively reduce computational time. Sosnovik and Oseledets (2017) first introduced the deep learning model to topology optimization and improved theefficiency of the optimization process by stating the problem as an image segmentation task.