  Natural Language Explanations (NLE) aim at supplementing the prediction of a
model with human-friendly natural text. Existing NLE approaches involve
training separate models for each downstream task. In this work, we propose
Uni-NLX, a unified framework that consolidates all NLE tasks into a single and
compact multi-task model using a unified training objective of text generation.
Additionally, we introduce two new NLE datasets: 1) ImageNetX, a dataset of
144K samples for explaining ImageNet categories, and 2) VQA-ParaX, a dataset of
123K samples for explaining the task of Visual Question Answering (VQA). Both
datasets are derived leveraging large language models (LLMs). By training on
the 1M combined NLE samples, our single unified framework is capable of
simultaneously performing seven NLE tasks including VQA, visual recognition and
visual reasoning tasks with 7X fewer parameters, demonstrating comparable
performance to the independent task-specific models in previous approaches, and
in certain tasks even outperforming them. Code is at
https://github.com/fawazsammani/uni-nlx
