Datasets

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TAL-SCQ5K are high-quality mathematical competition datasets created by TAL Education Group.

This dataset is a great resource for researchers who want to evaluate cross-lingual question answering performance.

The Benchmark of Linguistic Minimal Pairs, a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English, finds that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena.

Building machines with commonsense to compose realistically plausible sentences is challenging. CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday sce- nario using these concepts.

To create these datasets, the authors automatically translated the original CSQA and CODAH datasets, originally available only in English, into 15 other languages.

The DOCCI dataset consists of comprehensive descriptions on 15k images specifically taken with the objective of evaluating T2I and I2T models. These cover a lot of key details in the images, as illustrated below.

This is the repository for PLOD Dataset subset being used for CW in NLP module 2023-2024 at University of Surrey.

The ARC dataset consists of 7,787 science exam questions drawn from a variety of sources, including science questions provided under license by a research partner affiliated with AI2.

MNIST is used to train and evaluate image classification models in complex tasks.

NIH Chest X-Ray is a large dataset containing chest X-ray images of patients collected by the National Institutes of Health (NIH) of the United States.