From User Preferences to Optimization Constraints Using Large Language Models

• URL: http://arxiv.org/abs/2503.21360v1
• Date: Thu, 27 Mar 2025 10:52:10 GMT
• Title: From User Preferences to Optimization Constraints Using Large Language Models
• Abstract summary: This work explores using Large Language Models (LLMs) to translate user preferences into energy optimization constraints for home appliances.<n>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.<n>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.
• Score: 37.90220668060883
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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