A BIG-bench task that asks a model to judge whether a made-up sentence pairing an athlete with a sport-specific action is plausible or implausible.
unassessed
| Category | knowledge |
|---|---|
| Subcategory | commonsense plausibility judgement about sports actions and athletes |
| Page status | active |
| Metric | Multiple choice grade (accuracy on the plausible/implausible binary choice) |
| Direction | higher_is_better |
| Unit | accuracy |
| Dataset size | 986 |
| Publisher | Google (BIG-bench collaboration); task author Ethan Kim |
Sports Understanding presents a short statement combining a real athlete's name with a sport-specific action (and sometimes a competition), such as an athlete "threw a touchdown" or "scored a goal," and asks the model to classify the statement as plausible or implausible. Getting it right requires knowing which sport a given athlete plays and which actions are appropriate to that sport, i.e. domain-specific commonsense and sports knowledge rather than general reasoning.
Zero-shot binary multiple-choice classification: the model picks "plausible" or "implausible" for each of 986 combinatorially generated statements pairing an athlete with an action drawn from four major North American sports plus soccer, with some pairings sport-appropriate and others deliberately mismatched.
No model card in ModelSpec reports this benchmark yet.