Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

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 …

Current NLP models heavily rely on effective representation learning algorithms. Contrastive
learning is one such technique to learn an embedding space such that similar data sample …

The success of contrastive learning is well known to be dependent on data augmentation.
Although the degree of data augmentations has been well controlled by utilizing pre-defined …

Natural language inference (NLI) is an increasingly important task of natural language
processing, and the explainable NLI generates natural language explanations (NLEs) in …

Large language models (LLMs) such as GPT-4 are very powerful and can process different
kinds of natural language processing (NLP) tasks. However, it can be difficult to interpret the …

Existing knowledge-enhanced methods have achieved remarkable results in certain QA
tasks via obtaining diverse knowledge from different knowledge bases. However, limited by …

Large Language Models (LLMs) have recently made impressive strides in natural language
understanding tasks. Despite their remarkable performance, understanding their decision …

Natural Language Counterfactual generation aims to minimally modify a given text such that
the modified text will be classified into a different class. The generated counterfactuals …

Large Language Models (LLMs) have recently become proficient in addressing complex
tasks by utilizing their rich internal knowledge and reasoning ability. Consequently, this …