The code is a brief description of the WinoGrande dataset, which is a large-scale collection of Winograd Schema problems for commonsense reasoning. A Winograd Schema problem is a pair of sentences that differ in only one or two words and that contain an ambiguous pronoun that can be resolved differently depending on the context.

The WinoGrande dataset contains 44,000 problems, which are 160 times larger than the original Winograd Schema Challenge dataset. The problems are also more diverse and difficult, as they are generated by an adversarial filtering method that ensures that simple heuristics or word associations are not enough to solve them

The WinoGrande dataset is a challenging benchmark for evaluating the commonsense reasoning ability of large language models (LLMs), such as BERT, GPT-3, and RoBERTa. It tests the model’s ability to infer the correct referent of the pronoun based on the subtle differences in the sentences and the implicit knowledge about the world and human behavior. The WinoGrande dataset is also intended to encourage the development of more robust and generalizable models that can handle various domains and tasks.

• For example: The trophy would not fit in the brown suitcase because it was too big (small). What does “it” refer to, the trophy or the suitcase?
• Example: Here is an example problem from the WinoGrande dataset, taken from the art domain:
  • The statue was carefully removed from the pedestal because it was very old (heavy). What does “it” refer to, the statue or the pedestal?
    • The correct answer is the statue, as it is more likely to be removed because of its age or weight than the pedestal.

• The original paper by Sakaguchi et al. that introduces the dataset and the adversarial filtering method.
• The GitHub repository by Sakaguchi that contains the dataset, the code for adversarial filtering, and the models for WinoGrande.
• The Hugging Face website that provides the dataset download link and the documentation for loading and using the dataset.
• The Papers With Code website that lists the papers and code related to the WinoGrande dataset and task.
• The Allen Institute for AI website that provides the dataset download link and the leaderboard for the top-performing models.
• HellaSwag