Retrieval over Reasoning: A Cost-Controlled Benchmark of Language Models for Energy-Retrofit Recommendation
检索与推理：建筑节能改造推荐的成本受控模型基准AI 解读 ↓
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检索与推理：建筑节能改造推荐的成本受控模型基准
- 研究问题
- 以实际审计师推荐作为标签时，建筑节能措施推荐的准确度与成本如何受任务定义、检索增强和显式推理影响？
- 方法与思路
- 作者使用纽约市能源审计记录比较监督模型，将措施推荐设为多标签任务，并以二乘二设计分别切换语言模型的检索和显式推理。
- 材料陈述的贡献
- 摘要报告，多标签建模改善推荐表现，检索在所有受测语言模型上提高微平均 F1；显式推理未显示明显准确度改善且增加成本。
- 阅读关注点
- 应核对训练测试划分和检索库是否产生信息泄漏、审计师标签能否代表合理措施，以及不同任务定义下评价指标是否可比较。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或获期刊接收；结果针对所给审计数据与模型，不能据此判断真实节能或经济效益。
原文摘要
Recommending the correct set of energy conservation measures (ECMs) for a building is a structured, multi-label prediction problem in which a task-specific supervised model has weak training signal and a general language model has no grounding in the local building stock. We study this problem on 10,422 real New York City Local Law 87 (LL87) energy-audit records, taking as ground truth the set of ECM categories that certified auditors actually recommended. We make four contributions. First, we establish that energy-use-intensity (EUI) prediction - the upstream task - is effectively solved by tree ensembles: across fifteen trained models, a stacking ensemble reaches a coefficient of determination R^2 = 0.757, and every one of six neural architectures is outperformed by gradient-boosted trees. Second, we show that the framing of the recommendation task dominates model choice: recasting ECM recommendation as 19-way multi-label classification rather than single-label categorization lifts a gradient-boosted-tree baseline from a previously reported 25.9% accuracy to a micro-F1 of 0.571. Third, we benchmark eight large language models (LLMs) from four providers in a 2x2 design that independently toggles retrieval grounding and explicit reasoning, scoring each arm on per-label F1, U.S.-dollar cost per building, and latency; retrieval-augmented generation (RAG) improves micro-F1 by +0.11 to +0.20 on every model, while explicit reasoning yields no measurable accuracy change (-0.018 to +0.010) at up to 8.4x the cost. Fourth, we show LLMs systematically over-recommend - high recall, low precision - and that retrieval closes the gap chiefly by improving precision. A 70-billion-parameter open-weight model with a fifteen-line nearest-neighbor retrieval step reaches 0.511 micro-F1 at $0.00032 per building, comparable to a frontier model at roughly 10.1x lower cost.