language benchmark
CLUEWSC
CLUEWSC2020 is the Chinese version of the Winograd Schema Challenge, part of the CLUE benchmark. It focuses on pronoun disambiguation and coreference resolution, requiring models to determine which noun a pronoun refers to in a sentence. The dataset contains 1,244 training samples and 304 development samples extracted from contemporary Chinese literature.
Updated Aug 11, 2026
Models3
Model coverage3
MetricScore
EvidenceB
CLUEWSC Ranking
Higher score ranks better on this benchmark.
| 01 | MA | 91.4% | 100.0% | 3 | C | |
| 02 | DE | 90.9% | 50.0% | 3 | C | |
| 03 | BA | 48.6% | 0.0% | 3 | C |
CLUEWSC Score Distribution
A closer view of the leading scores on this benchmark.
CLUEWSC Highlights
The leading models and scores on this benchmark.
What is CLUEWSC?
What CLUEWSC measures and how its scores work.
CLUEWSC2020 is the Chinese version of the Winograd Schema Challenge, part of the CLUE benchmark. It focuses on pronoun disambiguation and coreference resolution, requiring models to determine which noun a pronoun refers to in a sentence. The dataset contains 1,244 training samples and 304 development samples extracted from contemporary Chinese literature.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- CLUEWSC
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- cluewsc|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about CLUEWSC.
Which model scores highest on CLUEWSC?
Kimi-k1.5 is currently ranked first with 91.4%.
What does CLUEWSC measure?
CLUEWSC2020 is the Chinese version of the Winograd Schema Challenge, part of the CLUE benchmark. It focuses on pronoun disambiguation and coreference resolution, requiring models to determine which noun a pronoun refers to in a sentence. The dataset contains 1,244 training samples and 304 development samples extracted from contemporary Chinese literature.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.