Auto71%Harbor adapterKnowledge & Reasoning

WSC

Winograd Schema Challenge

The Winograd Schema Challenge was introduced both as an alternative to the Turing Test and as a test of a system’s ability to do commonsense reasoning.

How to run

ltb · wsc
$ npm i -g lettokenburn-cli
$ ltb bench pull wsc --n 50
$ ltb bench run ./wsc -a claude-code -m anthropic/claude-opus-4-8
$ ltb bench push

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

16 sourced entries
HarnessSourceConfidenceLink
PaLM 540B89.5N/AIndependentmediumApr 5, 2022arxiv.org
PaLM 540B89.1N/AIndependentmediumApr 5, 2022arxiv.org
PaLM 2-M88.1N/AIndependentmediumMay 17, 2023arxiv.org
PaLM 2-L86.9N/AIndependentmediumMay 17, 2023arxiv.org
PaLM 540B86.3N/AIndependentmediumApr 5, 2022arxiv.org
PaLM 2-S84.6N/AIndependentmediumMay 17, 2023arxiv.org
GPT-3-175B80.1N/AIndependentmediumMay 28, 2020arxiv.org
UL2 20B79.9N/AIndependentmediumMay 10, 2022arxiv.org
GPT-2-XL 1.5B73.3N/AIndependentmediumApr 27, 2023arxiv.org
GPT-2-XL 1.5B70.7N/AIndependentmediumFeb 14, 2019d4mucfpksywv.cloudfront.net
OPT 1.3B64.2N/AIndependentmediumMay 24, 2024arxiv.org
OPT-125M59.6N/AIndependentmediumMay 24, 2024arxiv.org
Pythia 12B54.8N/AIndependentmediumApr 3, 2023arxiv.org
Pythia 2.8B38.5N/AIndependentmediumApr 3, 2023arxiv.org
Pythia 12B36.5N/AIndependentmediumApr 3, 2023arxiv.org
Pythia 6.9B36.5N/AIndependentmediumApr 3, 2023arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

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About

The Winograd Schema Challenge was introduced both as an alternative to the Turing Test and as a test of a system’s ability to do commonsense reasoning. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes.

The original Winograd Schema Challenge dataset consisted of 100 Winograd schemas constructed manually by AI experts. As of 2020 there are 285 examples available; however, the last 12 examples were only added recently. To ensure consistency with earlier models, several authors often prefer to report the performance on the first 273 examples only. These datasets are usually referred to as WSC285 and WSC273, respectively.

Source: https://arxiv.org/pdf/2004.13831.pdf Image Source: https://arxiv.org/pdf/1907.11983.pdf