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    <abstract>This work presents a comparison of some recently-released instruction-tuned large language models for Italian, focusing in particular on their effectiveness in a specific application scenario, i.e., that of delivering energy feedback. This work is part of a larger project aimed at developing a conversational interface for users of a renewable energy community, where clarity and accuracy of the provided feedback are important for a proper energy management. This comparison is based on the human evaluation of the output produced by such models using energy data as input. Specifically, the data pertains to information regarding the power flows within a household equipped with a photovoltaic (PV) plant and a battery storage system. The goal of the feedback is precisely that of providing the user with such information in a meaningful way based on the specific aspect they intend to monitor at a given moment (e.g., self-consumption levels, the power generated by the PV panels or imported from the main grid, or the battery state of charge). This evaluation experiment has the two-fold purpose of providing an exploratory analysis of the models’ abilities on this specific generation task solely relying on the information and instruction provided in the prompt, and as an initial investigation into their potential as reliable tools for generating user-friendly energy feedback in this intended scenario.</abstract>
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%0 Conference Proceedings
%T Assessing Italian Large Language Models on Energy Feedback Generation: A Human Evaluation Study
%A Sanguinetti, Manuela
%A Pani, Alessandro
%A Perniciano, Alessandra
%A Zedda, Luca
%A Loddo, Andrea
%A Atzori, Maurizio
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F sanguinetti-etal-2024-assessing
%X This work presents a comparison of some recently-released instruction-tuned large language models for Italian, focusing in particular on their effectiveness in a specific application scenario, i.e., that of delivering energy feedback. This work is part of a larger project aimed at developing a conversational interface for users of a renewable energy community, where clarity and accuracy of the provided feedback are important for a proper energy management. This comparison is based on the human evaluation of the output produced by such models using energy data as input. Specifically, the data pertains to information regarding the power flows within a household equipped with a photovoltaic (PV) plant and a battery storage system. The goal of the feedback is precisely that of providing the user with such information in a meaningful way based on the specific aspect they intend to monitor at a given moment (e.g., self-consumption levels, the power generated by the PV panels or imported from the main grid, or the battery state of charge). This evaluation experiment has the two-fold purpose of providing an exploratory analysis of the models’ abilities on this specific generation task solely relying on the information and instruction provided in the prompt, and as an initial investigation into their potential as reliable tools for generating user-friendly energy feedback in this intended scenario.
%U https://aclanthology.org/2024.clicit-1.95/
%P 880-887

Markdown (Informal)

[Assessing Italian Large Language Models on Energy Feedback Generation: A Human Evaluation Study](https://aclanthology.org/2024.clicit-1.95/) (Sanguinetti et al., CLiC-it 2024)

• Assessing Italian Large Language Models on Energy Feedback Generation: A Human Evaluation Study (Sanguinetti et al., CLiC-it 2024)

ACL

• Manuela Sanguinetti, Alessandro Pani, Alessandra Perniciano, Luca Zedda, Andrea Loddo, and Maurizio Atzori. 2024. Assessing Italian Large Language Models on Energy Feedback Generation: A Human Evaluation Study. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 880–887, Pisa, Italy. CEUR Workshop Proceedings.