Benchmark
Winogrande
reasoning
language
text
WinoGrande: An Adversarial Winograd Schema Challenge at Scale. A large-scale dataset of 44,000 pronoun resolution problems designed to test machine commonsense reasoning. Uses adversarial filtering to reduce spurious biases and provides a more robust evaluation of whether AI systems truly understand commonsense or exploit statistical shortcuts. Current best AI methods achieve 59.4-79.1% accuracy, significantly below human performance of 94.0%.
语言EN
满分1
参评模型19
模型排名
| 名次 | 模型 | 机构 | 分数 | 来源 |
|---|---|---|---|---|
| 1 | GPT-4 | OpenAI | 87.5 | 来源 ↗ |
| 2 | Command R+ | Cohere | 85.4 | 来源 ↗ |
| 3 | Qwen2 72B Instruct | Alibaba Cloud / Qwen Team | 85.1 | 来源 ↗ |
| 4 | Llama 3.1 Nemotron 70B Instruct | NVIDIA | 84.5 | 来源 ↗ |
| 5 | Gemma 2 27B | 83.7 | 来源 ↗ | |
| 6 | Qwen2.5 32B Instruct | Alibaba Cloud / Qwen Team | 82.0 | 来源 ↗ |
| 7 | Phi-3.5-MoE-instruct | Microsoft | 81.3 | 来源 ↗ |
| 8 | Qwen2.5-Coder 32B Instruct | Alibaba Cloud / Qwen Team | 80.8 | 来源 ↗ |
| 9 | Gemma 2 9B | 80.6 | 来源 ↗ | |
| 10 | Mistral NeMo Instruct | Mistral AI | 76.8 | 来源 ↗ |
| 11 | Ministral 8B Instruct | Mistral AI | 75.3 | 来源 ↗ |
| 12 | Granite 3.3 8B Base | IBM | 74.4 | 来源 ↗ |
| 13 | Qwen2.5-Coder 7B Instruct | Alibaba Cloud / Qwen Team | 72.9 | 来源 ↗ |
| 14 | Gemma 3n E4B Instructed LiteRT Preview | 71.7 | 来源 ↗ | |
| 15 | Gemma 3n E4B | 71.7 | 来源 ↗ | |
| 16 | Phi-3.5-mini-instruct | Microsoft | 68.5 | 来源 ↗ |
| 17 | Phi 4 Mini | Microsoft | 67.0 | 来源 ↗ |
| 18 | Gemma 3n E2B | 66.8 | 来源 ↗ | |
| 19 | Gemma 3n E2B Instructed LiteRT (Preview) | 66.8 | 来源 ↗ |