LLM-based iterative personalization boosted electricity conservation by 0.56 kWh per room-day (18.3 percentage-point higher adjusted saving rate) versus text nudges in a three-arm field trial.
Künzel, Jasjeet S
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
method 1
citation-polarity summary
years
2026 2representative citing papers
TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.
citing papers explorer
-
Enhancing behavioral nudges with large language model-based iterative personalization: A field experiment on electricity and hot-water conservation
LLM-based iterative personalization boosted electricity conservation by 0.56 kWh per room-day (18.3 percentage-point higher adjusted saving rate) versus text nudges in a three-arm field trial.
-
TabPFN-3: Technical Report
TabPFN-3 scales tabular foundation models to 1M rows with synthetic pretraining, test-time compute, and benchmark-leading performance on tabular, relational, and tabular-text tasks while being up to 20x faster than TabPFN-2.5.