MISeD (Meeting Information Seeking Dialogs dataset) is an information-seeking dialog dataset focused on meeting transcripts. It includes 432 dialogs over transcripts from the QMSum dataset. MISeD is described in detail in the paper: Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts.

RichHF-18K dataset contains rich human feedback labels we collected for our CVPR'24 paper: https://arxiv.org/pdf/2312.10240, along with the file name of the associated labeled images (no urls or images are included in this dataset).

Images gathered from the Internet in 2023 and some metadata

GeniL dataset is an effort for detecting various types of generalization in language. This multilingual dataset covers sentences in EN, FR, ES, PT, AR, HI, BN, MS, and ID and is annotated by native speakers of each language. Each sentence is collected from a public corpora of language and contains at least one identity group name and an attribute.

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).

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.

D3code is a large-scale cross-cultural dataset of parallel annotations for offensive language detection by over 4k annotators, balanced across gender and age, from across 21 countries, representing eight geo-cultural regions.

Visage contains an image dataset of images with human annotations on whether or not certain attributes are present or depicted in the image. The attribute may either be stereotypical or non-stereotypical w.r.t. to the identity group in the image. It also contains a list of attributes in English along with annotations about whether they are visual.