Visual reasoning requires multimodal perception and commonsense cognition of the world.
Recently, multiple vision-language models (VLMs) have been proposed with excellent …

Large-scale vision and language representation learning has shown promising
improvements on various vision-language tasks. Most existing methods employ a …

With the development of artificial intelligence, more and more people hope that computers
can understand human language through natural language technology, learn to think like …

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 …

Abstract Vision-Language Pre-training (VLP) aims to learn multi-modal representations from
image-text pairs and serves for downstream vision-language tasks in a fine-tuning fashion …

Natural language explanation (NLE) models aim at explaining the decision-making process
of a black box system via generating natural language sentences which are human-friendly …

Natural language is an appealing medium for explaining how large language models
process and store information, but evaluating the faithfulness of such explanations is …

Natural language rationales could provide intuitive, higher-level explanations that are easily
understandable by humans, complementing the more broadly studied lower-level …