From User Preferences to Optimization Constraints Using Large Language Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917969122033664 |
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| author | Sanguinetti, Manuela Perniciano, Alessandra Zedda, Luca Loddo, Andrea Di Ruberto, Cecilia Atzori, Maurizio |
| author_facet | Sanguinetti, Manuela Perniciano, Alessandra Zedda, Luca Loddo, Andrea Di Ruberto, Cecilia Atzori, Maurizio |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21360 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From User Preferences to Optimization Constraints Using Large Language Models Sanguinetti, Manuela Perniciano, Alessandra Zedda, Luca Loddo, Andrea Di Ruberto, Cecilia Atzori, Maurizio Computation and Language 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 |
| title | From User Preferences to Optimization Constraints Using Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.21360 |