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The Mysterious Datasets Behind GPT Models - BoolQ, PIQA, HellaSwag, and More

Artificial intelligence has made tremendous progress over the past few years, with GPT models in the natural language processing (NLP) field receiving particular attention. The GPT series, based on the Transformer architecture, has demonstrated outstanding language generation capabilities and found applications across a wide range of domains. Behind these models lie several important datasets — BoolQ, PIQA, HellaSwag, WinoGrande, and the ARC series — which have provided invaluable resources for the development and application of GPT models.

BoolQ: True or False?

BoolQ is a dataset designed for natural language inference (NLI) tasks, testing a model's ability to answer yes-or-no questions. The dataset contains 15,942 samples. These questions are naturally occurring — generated in an unprompted, unconstrained environment. Each entry consists of a question and a corresponding passage, and the model must determine whether the answer is true or false based on the passage. BoolQ not only tests the model's natural language understanding but also requires strong reasoning and judgment skills, making it significant for NLI research.

Dataset URL

https://huggingface.co/datasets/boolq

Sample Example

Question: "is windows movie maker part of windows essentials"

Answer: Yes

Passage: "Windows Movie Maker (formerly known as Windows Live Movie Maker in Windows 7) is a discontinued video editing software by Microsoft. It is a part of Windows Essentials software suite and offers the ability to create and edit videos as well as to publish them on OneDrive, Facebook, Vimeo, YouTube, and Flickr."

PIQA: Physical Interaction Challenges

PIQA, short for "Physical Interaction: Question Answering," is a dataset focused on physical world reasoning. Its goal is to evaluate a model's ability to handle questions involving physical interactions. These questions require the model to understand the principles and laws of the physical world and infer the correct answer. PIQA enables researchers to better explore how models handle physical interaction scenarios, providing valuable references for real-world applications.

Dataset URL

https://huggingface.co/datasets/piqa

Sample Example

Goal: When boiling butter, when it's ready, you can

Solution 1: Pour it onto a plate

Solution 2: Pour it into a jar

HellaSwag: Commonsense Reasoning Challenges

HellaSwag is a challenging commonsense reasoning dataset designed to prevent models from guessing answers based on surface-level cues. Unlike other datasets, HellaSwag constructs adversarial (fake) answers that encourage the model to perform deeper commonsense reasoning rather than relying on simple pattern matching. This forces the model to demonstrate higher-level reasoning when answering questions, providing a more rigorous test of its intelligence.

Dataset URL

https://huggingface.co/datasets/hellaswag

Sample Example

Activity: Removing ice from car

Context: Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles.

Ending Options: - ", the man adds wax to the windshield and cuts it." - ", a person board a ski lift, while two men supporting the head of the person wearing winter clothes snow as the we girls sled." - ", the man puts on a christmas coat, knitted with netting." - ", the man continues removing the snow on his car."

WinoGrande: A New Commonsense Reasoning Challenge

WinoGrande is a reasoning-based natural language understanding dataset used to evaluate a model's ability to handle commonsense reasoning and association problems. Compared to other common NLP datasets, WinoGrande's questions are more complex, requiring deeper commonsense reasoning and contextual understanding. This forces the model to demonstrate stronger intelligent reasoning abilities, expanding into new areas of NLP research.

Dataset URL

https://huggingface.co/datasets/winogrande

Sample Example (fill-in-the-blank)

Sentence: John moved the couch from the garage to the backyard to create space. The _ is small.

Option 1: garage

Option 2: backyard

Answer: 1

ARC Dataset: Advanced Commonsense Reasoning

The ARC dataset contains 7,787 genuine elementary-school-level multiple-choice science questions, collected to encourage advanced question-answering research. The ARC series includes two versions: ARC-e (ARC Easy) and ARC-c (ARC Challenge). ARC-e provides relatively simpler questions for initial model evaluation, while ARC-c is more challenging, with more complex questions requiring deeper commonsense reasoning and inference. This pushes models to continuously evolve and improve to better handle complex reasoning scenarios.

Dataset URL

https://huggingface.co/datasets/vietgpt/ARC-Challenge_en

Sample Example

Question: George wants to warm his hands quickly by rubbing them. Which skin surface will produce the most heat?

Options:

Answer: A

Summary

Throughout the development of GPT models, the datasets described above have played a crucial role, providing benchmarks and challenges for evaluating and improving model performance. At the same time, these datasets have driven continuous innovation and progress in the NLP field, advancing natural language processing technology to unprecedented levels. Looking ahead, we expect these datasets to continue providing new insights and breakthroughs for GPT models and broader NLP research, contributing more wisdom and strength to the development of AI.