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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add MultiNLI intermediate-task transfer | BERT · BoolQ | accuracy: 77.4 -> 79.0 | Helped |
| Add MultiNLI intermediate-task transfer | BERT · CB | F1: 75.7 -> 84.7 | Helped |
| Add MultiNLI intermediate-task transfer | BERT · RTE | accuracy: 71.6 -> 79.0 | Helped |
| Add SWAG intermediate-task transfer | BERT · COPA | accuracy: 70.6 -> 73.8 | Helped |
| Use BERT instead of the simple baselines | SuperGLUE tasks · SuperGLUE | average score: BERT increases the average SuperGLUE score by 25 points | Helped |
| Use BERT without data augmentation on WSC rather than simple baselines | WSC · WSC | accuracy | Hurt |
Closest papers by meaning
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- metabench - A Sparse Benchmark of Reasoning and Knowledge in Large Language Models (ICLR 2025 · similarity 0.85)
- INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge (ICLR 2025 · similarity 0.85)
- MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark (ICLR 2026 · similarity 0.84)
- Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages (AAAI 2025 · similarity 0.84)
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