Omkar2118/datasets huggingface dataset:
folktexts
Full name: folktexts

A collection of natural language prompt-completion pairs pertaining to multiple-choice Q&A on benchmark tasks based on US census products.
Benchmark tasks are made available through a python package dubbed `folktexts`.
The main goal is to serve as a basis to evaluate LLMs' capabilities of uncertainty quantification on uncertain outcomes, i.e., evaluating quantification of _aleatoric_ uncertainty.
This is essentially a natural-language version of the popular _folktables_ tabular data package.

Introducing paper: Evaluating language models as risk scores
Morpho-MNIST
Full name: Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations. To address this issue we introduce Morpho-MNIST, a framework that aims to answer: "to what extent has my model learned to represent specific factors of variation in the data?" We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of trained models, identification of the roles of latent variables, and characterisation of sample diversity. We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation. Data and code are available at https://github.com/dccastro/Morpho-MNIST.