Summary of Question Answering For Decisionmaking in Green Building Design: a Multimodal Data Reasoning Method Driven by Large Language Models, By Yihui Li et al.
Question Answering for Decisionmaking in Green Building Design: A Multimodal Data Reasoning Method Driven by Large Language Models
by Yihui Li, Xiaoyue Yan, Hao Zhou, Borong Lin
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to green building design decision-making is proposed, integrating large language models with performance simulation. GreenQA, a question answering framework, utilizes multimodal data reasoning to enable weather data analysis and visualization, case retrieval, and knowledge querying. The platform’s effectiveness in improving design efficiency was validated through user surveys, with 96% of users agreeing that it helped streamline the process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being explored to make green building design more efficient. This involves combining big language models with computer simulations. The result is a tool called GreenQA that can analyze and visualize weather data, find examples of successful buildings, and answer questions about sustainable design. People who tested this system found it made designing buildings easier and faster. |
Keywords
» Artificial intelligence » Question answering
