Neural networks trained on biased datasets tend to inadvertently learn spurious correlations, hindering generalization.

The inherent assumption in IO is that an agent generates its decision by solving an optimization problem.

The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning.

On one hand, it operates as a general data augmentation strategy.

Any explanation that faithfully explains this type of model needs to be in agreement with this invariance property.

Models (LLMs) can sometimes surpass those annotated by experts, such as journalists, according to human evaluations. However, there is limited research on whether these generic summaries meet the individual needs of ordinary people.