More than one hundred benchmarks have been developed to test the commonsense
knowledge and commonsense reasoning abilities of artificial intelligence (AI) systems …

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 …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

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

Foundation models have received much attention due to their effectiveness across a broad
range of downstream applications. Though there is a big convergence in terms of …

Chain-of-thought prompting (eg," Let's think step-by-step") primes large language models to
verbalize rationalization for their predictions. While chain-of-thought can lead to dramatic …

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 …

Instruction tuning has significantly advanced large language models (LLMs) such as
ChatGPT, enabling them to align with human instructions across diverse tasks. However …

Multimodal large language models (MLLMs) enhance the capabilities of standard large
language models by integrating and processing data from multiple modalities, including text …