UICrit is a dataset containing human-generated natural language design critiques, corresponding bounding boxes for each critique, and design quality ratings for 1,000 mobile UIs from RICO. This dataset was collected for our UIST '24 paper: https://arxiv.org/abs/2407.08850.

The Tap Typing with Touch Sensing Images (TSI) dataset contains data of user taps on a mobile touchscreen keyboard, including elliptical features and capacitive sensing images of the taps. The dataset aligns each tap with a key the user intended to type during data collection so it can be used for keyboard decoder training and/or evaluation.

Datasets for measuring misgendering in translation

This dataset contains results from all rounds of Adversarial Nibbler. This data includes adversarial prompts fed into public generative text2image models and validations for unsafe images. There will be two sets of data: all prompts submitted and all prompts attempted (sent to t2i models but not submitted as unsafe).

WIT (Wikipedia-based Image Text) Dataset is a large multimodal multilingual dataset comprising 37M+ image-text sets with 11M+ unique images across 100+ languages.

This dataset contains synthetic training data for grammatical error correction. The corpus is generated by corrupting clean sentences from C4 using a tagged corruption model. The approach and the dataset are described in more detail by Stahlberg and Kumar (2021) (https://www.aclweb.org/anthology/2021.bea-1.4/)

SeeGULL Multilingual is a multilingual and multicultural dataset of stereotypes. It consists of stereotypes in 20 languages with human annotations across 23 languages, including annotations on their degree of offensiveness.

ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. We hope it can serve as a useful research benchmark for high-precision conditional text generation.

IndicGenBench is a high-quality, multilingual, multi-way parallel benchmark for evaluating Large Language Models (LLMs) on 4 user-facing generation tasks across a diverse set 29 of Indic languages covering 13 scripts and 4 language families.