用大模型将用户口语需求转为家电节能约束，提升智能社区能源效率
From User Preferences to Optimization Constraints Using Large Language Models
- 通过大模型将意大利语用户语音转化为家电能耗约束
- 在零样本、少样本设置下验证了模型在真实场景中的有效性
- 公开数据集与代码，助力智能能源系统研究
本研究探索使用大语言模型（LLM）将用户偏好转化为家庭电器的能源优化约束。在意大利可再生能源社区（REC）背景下，将自然语言用户表述转换为智能家电的正式约束。评估了当前可用的多种意大利语LLM在零样本、单样本和少样本学习设置下的表现，基于一个包含意大利用户请求及其对应形式化约束表示的试点数据集。贡献包括建立该任务的基线性能，公开发布数据集与代码以促进后续研究，并提供关于LLM在此领域最佳实践与局限性的洞察。
原文摘要 · Abstract (English)
This work explores using Large Language Models (LLMs) to translate user preferences into energy optimization constraints for home appliances. We describe a task where natural language user utterances are converted into formal constraints for smart appliances, within the broader context of a renewable energy community (REC) and in the Italian scenario. We evaluate the effectiveness of various LLMs currently available for Italian in translating these preferences resorting to classical zero-shot, one-shot, and few-shot learning settings, using a pilot dataset of Italian user requests paired with corresponding formal constraint representation. Our contributions include establishing a baseline performance for this task, publicly releasing the dataset and code for further research, and providing insights on observed best practices and limitations of LLMs in this particular domain
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