piqa-eval
PIQA: Reasoning about Physical Commonsense in Natural Language — Bisk et al. (2019) (arXiv:1911.11641, 2019)
What this evaluates
Evaluates a model's ability to reason about physical commonsense and intuitive physics by selecting the correct solution for a given goal from two options. It probes understanding of object affordances, material properties, and non-prototypical uses of everyday items.
Datasets
- PIQA — total ?; splits: train (-1), val (-1), test (-1)
Metrics
Accuracy(primary) — range: percent- Percentage of correctly predicted options out of the total number of instances. Computed as a binary classification accuracy over two candidate solutions.
Input / output format
Input: A goal description followed by two candidate solutions (Option A and Option B), processed with a [CLS] token for each choice.
Output: A binary classification prediction indicating whether Option A or Option B is the correct solution.
Scoring recipe
correct = 0
for pred, gold in zip(predictions, gold_labels):
if pred == gold:
correct += 1
accuracy = (correct / len(gold_labels)) * 100
Common pitfalls
- Some model 'mistakes' are actually correct but require external web search to verify, making strict accuracy evaluation potentially misleading.
- Finetuning can be highly unstable with certain hyperparameters, sometimes dropping validation performance to chance levels.
- Human performance was only evaluated on the development set, not the test set, so direct human-to-model comparison on test data is not available.
Evidence (verbatim from paper)
Table 1: Results of state-of-the-art natural language understanding models on PIQA, compared with human performance. The results show a significant gap between model and human performance, of roughly 20 absolute points. Accuracy (%) Validation Test
Citation
@misc{bisk2019piqa,
title={PIQA: Reasoning about Physical Commonsense in Natural Language},
author={Bisk et al. (2019)},
year={2019},
note={arXiv:1911.11641}
}
- arXiv: 1911.11641