The plausibility machine commonsense (PMC) dataset: A massively crowdsourced human-annotated dataset for studying plausibility in large language models.

Commonsense reasoning has emerged as a challenging problem in Artificial Intelligence (AI). However, one area of commonsense reasoning that has not received nearly as much attention in the AI research community is , which focuses on determining the likelihood of commonsense statements. Human-annotated benchmarks are essential for advancing research in this nascent area, as they enable researchers to develop and evaluate AI models effectively. Because plausibility is a subjective concept, it is important to obtain nuanced annotations, rather than a binary label of 'plausible' or 'implausible'. Furthermore, it is also important to obtain multiple human annotations for a given statement, to ensure validity of the labels. In this data article, we describe the process of re-annotating an existing commonsense plausibility dataset (SemEval-2020 Task 4) using large-scale crowdsourcing on the Amazon Mechanical Turk platform. We obtain 10,000 unique annotations on a corpus of 2000 sentences (five independent annotations per sentence). Based on these labels, each was labelled as . Next, we prompted the GPT-3.5 and GPT-4 models developed by OpenAI. Sentences from the human-annotated files were fed into the models using custom prompt templates, and the models' generated labels were used to determine if they were aligned with those output by humans. The PMC-Dataset is meant to serve as a rich resource for analysing and comparing human and machine commonsense reasoning capabilities, specifically on plausibility. Researchers can utilise this dataset to train, fine-tune, and evaluate AI models on plausibility. Applications include: determining the likelihood of everyday events, assessing the realism of hypothetical scenarios, and distinguishing between plausible and implausible statements in commonsense text. Ultimately, we intend for the dataset to support ongoing AI research by offering a robust foundation for developing models that are better aligned with human commonsense reasoning.