This work addresses the challenge of providing consistent explanations for
predictive models in the presence of model indeterminacy, which arises due to
the existence of multiple (nearly) equally well-performing
Large language models have the ability to quickly adapt to target tasks without gradient updates by using an Explanation-Aware Soft Ensemble framework, which improves the consistency between explanations and final predictions.