LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction
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Abstract (arXiv)
Question-answering (QA) interfaces powered by large language models (LLMs) present a promising direction for improving interactivity with HVAC system insights, particularly for non-expert users. However, enabling accurate, real-time, and context-aware interactions with HVAC systems introduces unique challenges, including the integration of frequently updated sensor data, domain-specific knowledge grounding, and coherent multi-stage reasoning. In this paper, we present JARVIS, a two-stage LLM-based QA framework tailored for sensor data-driven HVAC system interaction. JARVIS employs an Expert-LLM to translate high-level user queries into structured execution instructions, and an Agent that performs SQL-based data retrieval, statistical processing, and final response generation. To address HVAC-specific challenges, JARVIS integrates (1) an adaptive context injection strategy for efficient HVAC and deployment-specific information integration, (2) a parameterized SQL builder and executor to improve data access reliability, and (3) a bottom-up planning scheme to ensure consistency across multi-stage response generation. We evaluate JARVIS using real-world data collected from a commercial HVAC system and a ground truth QA dataset curated by HVAC experts to demonstrate its effectiveness in delivering accurate and interpretable responses across diverse queries. Results show that JARVIS consistently outperforms baseline and ablation variants in both automated and user-centered assessments, achieving high response quality and accuracy.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 7 Jul 2025.
Builds on
21 of 49 references.
References · 21
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
- 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
- 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
- 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
- 2024LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methodscited by 465
- 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
- 2024Seven Failure Points When Engineering a Retrieval Augmented Generation Systemcited by 139
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- 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
- 2024Make Your LLM Fully Utilize the Contextcited by 119
- 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
- 2024EXAONE 3.5: Series of Large Language Models for Real-world Use Casescited by 53
- 2025A Survey of Automatic Prompt Engineering: An Optimization Perspectivecited by 41
- 2025TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecastercited by 40
- 2024Text2SQL is Not Enough: Unifying AI and Databases with TAGcited by 34
- 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practicescited by 23
- 2015HVACMeter: Apportionment of HVAC Power to Thermal Zones and Air Handler Unitscited by 1
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Citing papers per year: 2026: 2.
2 papers.