PIQA is a benchmark dataset for evaluating physical commonsense reasoning in natural language understanding. The dataset consists of multiple-choice questions where the correct answer requires understanding of physical principles. Humans achieve high accuracy (95%) on the dataset, while large pretrained models like BERT and RoBERTa perform significantly worse (around 77%). The dataset was created to test the ability of language models to reason about physical interactions and to identify gaps in their understanding of physical commonsense. The PIQA dataset is built from real-world scenarios and instructions, focusing on everyday situations and atypical solutions. It includes a wide range of physical phenomena and requires models to understand the properties of objects, their affordances, and how they can be manipulated. The dataset is designed to challenge models by requiring them to reason about physical interactions, often involving subtle misunderstandings of preconditions or physics. The dataset includes a variety of question-answer pairs, with the goal often indicating a post-condition and the solutions indicating the procedure for accomplishing this. The dataset is designed to test the ability of models to understand and apply physical commonsense knowledge, which is critical for tasks such as problem-solving and expressing needs. The PIQA dataset was created to evaluate the performance of state-of-the-art natural language understanding models on physical commonsense reasoning tasks. The results show that these models struggle with physical commonsense questions, highlighting the need for further research into building language representations that capture detailed knowledge of the physical world. The dataset includes a variety of question-answer pairs, with the goal often indicating a post-condition and the solutions indicating the procedure for accomplishing this. The dataset is designed to test the ability of models to understand and apply physical commonsense knowledge, which is critical for tasks such as problem-solving and expressing needs. The results show that models like RoBERTa struggle with certain physical concepts and relations, indicating the need for further research into building language representations that capture detailed knowledge of the physical world.PIQA is a benchmark dataset for evaluating physical commonsense reasoning in natural language understanding. The dataset consists of multiple-choice questions where the correct answer requires understanding of physical principles. Humans achieve high accuracy (95%) on the dataset, while large pretrained models like BERT and RoBERTa perform significantly worse (around 77%). The dataset was created to test the ability of language models to reason about physical interactions and to identify gaps in their understanding of physical commonsense. The PIQA dataset is built from real-world scenarios and instructions, focusing on everyday situations and atypical solutions. It includes a wide range of physical phenomena and requires models to understand the properties of objects, their affordances, and how they can be manipulated. The dataset is designed to challenge models by requiring them to reason about physical interactions, often involving subtle misunderstandings of preconditions or physics. The dataset includes a variety of question-answer pairs, with the goal often indicating a post-condition and the solutions indicating the procedure for accomplishing this. The dataset is designed to test the ability of models to understand and apply physical commonsense knowledge, which is critical for tasks such as problem-solving and expressing needs. The PIQA dataset was created to evaluate the performance of state-of-the-art natural language understanding models on physical commonsense reasoning tasks. The results show that these models struggle with physical commonsense questions, highlighting the need for further research into building language representations that capture detailed knowledge of the physical world. The dataset includes a variety of question-answer pairs, with the goal often indicating a post-condition and the solutions indicating the procedure for accomplishing this. The dataset is designed to test the ability of models to understand and apply physical commonsense knowledge, which is critical for tasks such as problem-solving and expressing needs. The results show that models like RoBERTa struggle with certain physical concepts and relations, indicating the need for further research into building language representations that capture detailed knowledge of the physical world.