Pith Number

pith:TO7S2ELH

pith:2019:TO7S2ELHTBHW4FYY2WRTOASAOK

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PIQA: Reasoning about Physical Commonsense in Natural Language

Jianfeng Gao, Ronan Le Bras, Rowan Zellers, Yejin Choi, Yonatan Bisk

Large pretrained models reach only 77 percent accuracy on physical commonsense questions that humans answer at 95 percent.

arxiv:1911.11641 v1 · 2019-11-26 · cs.CL · cs.AI · cs.LG

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

large pretrained models struggle (77%). We provide analysis about the dimensions of knowledge that existing models lack, which offers significant opportunities for future research.

C2weakest assumption

That the collected PIQA questions genuinely require physical commonsense reasoning and cannot be solved primarily through linguistic patterns or reporting bias present in the training data.

C3one line summary

PIQA is a new benchmark showing that current AI models achieve 77% on physical commonsense questions versus humans at 95%.

References

80 extracted · 80 resolved · 4 Pith anchors

[1] CVPR , year =

[2] SocialIQA: Commonsense Reasoning about Social Interactions , booktitle = 2019

[3] WINOGRANDE: An Adversarial Winograd Schema Challenge at Scale , author=. AAAI , year=

[4] ACL , year =

[5] IROS , year =

Formal links

2 machine-checked theorem links

Cited by

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Receipt and verification

First computed	2026-05-17T23:38:13.765123Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9bbf2d1167984f6e1718d5a337024072854ec4e228b8b3380322fd6fb7d9eff6

Aliases

arxiv: 1911.11641 · arxiv_version: 1911.11641v1 · doi: 10.48550/arxiv.1911.11641 · pith_short_12: TO7S2ELHTBHW · pith_short_16: TO7S2ELHTBHW4FYY · pith_short_8: TO7S2ELH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TO7S2ELHTBHW4FYY2WRTOASAOK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9bbf2d1167984f6e1718d5a337024072854ec4e228b8b3380322fd6fb7d9eff6

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2019-11-26T15:31:46Z",
    "title_canon_sha256": "e3a4aee1cb2d205fde52f4d934479050fda3d115f107a611a5e692b7161df23d"
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