Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

This article presents a comprehensive and practical guide for practitioners and end-users
working with Large Language Models (LLMs) in their downstream Natural Language …

Image captioning, also called report generation in medical field, aims to describe visual
content of images in human language, which requires to model semantic relationship …

Recently, DALL-E, a multimodal transformer language model, and its variants including
diffusion models have shown high-quality text-to-image generation capabilities. However …

Image captioning is an important task for benchmarking visual reasoning and for enabling
accessibility for people with vision impairments. However, as in many machine learning …

The increasing tendency to collect large and uncurated datasets to train vision-and-
language models has raised concerns about fair representations. It is known that even small …

Interpretability in machine learning models is important in high-stakes decisions such as
whether to order a biopsy based on a mammographic exam. Mammography poses …

We study societal bias amplification in image captioning. Image captioning models have
been shown to perpetuate gender and racial biases, however, metrics to measure, quantify …

Abductive reasoning seeks the likeliest possible explanation for partial observations.
Although abduction is frequently employed in human daily reasoning, it is rarely explored in …

Large language models (LLMs) have recently shown great potential for in-context learning,
where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts) …