Multi-document summarization (MDS) is an effective tool for information aggregation that
generates an informative and concise summary from a cluster of topic-related documents …

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …

State-of-the-art models in NLP are now predominantly based on deep neural networks that
are opaque in terms of how they come to make predictions. This limitation has increased …

Integrating free-text explanations to in-context learning of large language models (LLM) is
shown to elicit strong reasoning capabilities along with reasonable explanations. In this …

Feature attribution methods are popular in interpretable machine learning. These methods
compute the attribution of each input feature to represent its importance, but there is no …

In interpretable NLP, we require faithful rationales that reflect the model's decision-making
process for an explained instance. While prior work focuses on extractive rationales (a …

Modern deep learning models for NLP are notoriously opaque. This has motivated the
development of methods for interpreting such models, eg, via gradient-based saliency maps …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Humans have been shown to give contrastive explanations, which explain why an observed
event happened rather than some other counterfactual event (the contrast case). Despite the …

A growing line of work has investigated the development of neural NLP models that can
produce rationales--subsets of input that can explain their model predictions. In this paper …