Virtual Mental Health Assistants (VMHAs) continuously evolve to support the overloaded
global healthcare system, which receives approximately 60 million primary care visits and 6 …

Large language models are increasingly capable of generating fluent-appearing text with
relatively little task-specific supervision. But can these models accurately explain …

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 …

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 …

As the use of deep learning techniques has grown across various fields over the past
decade, complaints about the opaqueness of the black-box models have increased …

Generating short answer questions is a popular form of learnersourcing with benefits for
both the students' higher-order thinking and the instructors' collection of assessment items …

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 …

Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear
what constitutes a good reasoning chain and how to evaluate them. Most existing methods …

Recent studies have exploited advanced generative language models to generate Natural
Language Explanations (NLE) for why a certain text could be hateful. We propose the Chain …

Many research topics in natural language processing (NLP), such as explanation
generation, dialog modeling, or machine translation, require evaluation that goes beyond …