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Found 5 papers, 0 papers with code
Leveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles
Prior work has demonstrated that interventions for behavior must be personalized, and that the intervention that is most effective on average across a large group can result in a backlash effect that strengthens opposition among some subgroups.
Using LLMs to Model the Beliefs and Preferences of Targeted Populations
We consider the problem of aligning a large language model (LLM) to model the preferences of a human population.
Understanding and Shifting Preferences for Battery Electric Vehicles
In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs).
Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression
We develop a deep neural network (MACSYMA) to address the symbolic regression problem as an end-to-end supervised learning problem.
Machine learning reveals how personalized climate communication can both succeed and backfire
Different advertising messages work for different people.
