Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Recent advances in deep learning have improved the performance of many Natural
Language Processing (NLP) tasks such as translation, question-answering, and text …

The importance and pervasiveness of emotions in our lives makes affective computing a
tremendously important and vibrant line of work. Systems for automatic emotion recognition …

An extractive rationale explains a language model's (LM's) prediction on a given task
instance by highlighting the text inputs that most influenced the prediction. Ideally, rationale …

Abstract Explainable Artificial Intelligence refers to developing artificial intelligence models
and systems that can provide clear, understandable, and transparent explanations for their …

Rationale extraction can be considered as a straightforward method of improving the model
explainability, where rationales are a subsequence of the original inputs, and can be …

We present a novel feature attribution method for explaining text classifiers, and analyze it in
the context of hate speech detection. Although feature attribution models usually provide a …

While natural language systems continue improving, they are still imperfect. If a user has a
better understanding of how a system works, they may be able to better accomplish their …

The opaqueness of the multi-hop fact verification model imposes imperative requirements
for explainability. One feasible way is to extract rationales, a subset of inputs, where the …

Large language models (LLMs) often generate inaccurate or fabricated information and
generally fail to indicate their confidence, which limits their broader applications. Previous …