WinoGrande
DatasetFree44K pronoun resolution problems testing commonsense understanding.
Capabilities6 decomposed
adversarially-filtered pronoun resolution benchmark construction
Medium confidenceConstructs 44,000 pronoun resolution problems by applying adversarial filtering techniques to eliminate dataset artifacts, statistical biases, and spurious correlations that allow models to succeed without genuine commonsense reasoning. Uses human annotation and automated bias detection to ensure problems require deep semantic understanding rather than surface-level pattern matching or lexical shortcuts.
Uses adversarial filtering pipeline specifically designed to remove dataset artifacts and statistical biases that allow models to solve problems without genuine commonsense understanding, rather than collecting raw examples that may contain spurious correlations. Incorporates human-in-the-loop validation to ensure problems require semantic reasoning.
More robust than original Winograd Schema Challenge because it explicitly filters against statistical shortcuts and dataset artifacts, making it harder for models to achieve high accuracy through pattern matching rather than true commonsense reasoning.
commonsense reasoning evaluation harness integration
Medium confidenceIntegrates into standard LLM evaluation frameworks (HELM, LM Evaluation Harness, etc.) as a drop-in benchmark task with standardized metrics, making it trivial for researchers to include WinoGrande in multi-benchmark evaluation suites. Provides structured problem format compatible with multiple-choice evaluation pipelines and aggregates results across problem categories.
Pre-integrated into major evaluation frameworks (HELM, LM Evaluation Harness) with standardized task definitions and metric computation, eliminating custom integration work. Provides consistent problem formatting and result aggregation across different evaluation platforms.
Faster to include in comprehensive evaluation suites than custom-built reasoning benchmarks because it's already integrated into standard harnesses with pre-defined metrics and problem formatting.
multi-category commonsense reasoning stratification
Medium confidenceStratifies 44,000 problems across multiple commonsense reasoning categories (entity relationships, temporal reasoning, physical properties, social dynamics, etc.), enabling fine-grained analysis of which reasoning types models struggle with. Allows researchers to identify capability gaps in specific commonsense domains rather than treating reasoning as monolithic.
Explicitly stratifies problems across multiple commonsense reasoning categories with human-validated annotations, enabling category-level performance analysis rather than treating all problems as equivalent. Allows researchers to identify which reasoning types drive overall performance differences.
Provides more diagnostic insight than single-score benchmarks because category-level breakdowns reveal which reasoning types models struggle with, enabling targeted improvements rather than black-box optimization.
human-performance baseline calibration
Medium confidenceIncludes human performance baseline of 94% accuracy collected through crowdsourced annotation, providing a calibrated upper bound for model evaluation and enabling meaningful comparison of model performance relative to human capability. Allows researchers to assess whether models are approaching human-level reasoning or falling significantly short.
Provides crowdsourced human performance baseline (94%) collected through the same annotation process as problem creation, enabling direct comparison of model performance against human capability on identical problems. Baseline is published with dataset, making it standard reference point.
More meaningful than benchmarks without human baselines because it contextualizes model performance relative to human capability, making it clear whether models are approaching human-level reasoning or significantly underperforming.
bias-resistant problem generation and validation
Medium confidenceApplies automated bias detection and adversarial filtering during problem generation to eliminate statistical shortcuts (e.g., gender bias, word frequency bias, lexical overlap bias) that allow models to succeed without genuine reasoning. Uses human validation to confirm that remaining problems require commonsense understanding rather than exploiting dataset artifacts.
Applies explicit adversarial filtering pipeline to remove problems solvable through statistical shortcuts, gender bias, word frequency bias, and other dataset artifacts. Uses human validation to confirm filtered problems require genuine commonsense reasoning rather than exploiting spurious correlations.
More robust than unfiltered benchmarks because it explicitly removes problems solvable through statistical shortcuts, making high model performance more meaningful as evidence of genuine reasoning capability rather than bias exploitation.
large-scale commonsense reasoning dataset curation
Medium confidenceCurates and validates 44,000 pronoun resolution problems at scale through combination of automated generation, human annotation, and quality control processes. Manages dataset versioning, documentation, and distribution through HuggingFace, enabling reproducible research and easy integration into evaluation pipelines.
Manages 44,000 curated problems as a versioned, documented dataset distributed through HuggingFace, enabling one-line integration into research workflows. Includes metadata, splits, and documentation for reproducible research.
Easier to use than custom-built benchmarks because it's pre-curated, versioned, and distributed through HuggingFace with standardized formatting, eliminating dataset construction overhead.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with WinoGrande, ranked by overlap. Discovered automatically through the match graph.
HellaSwag
70K commonsense reasoning questions with adversarial distractors.
BIG-Bench Hard (BBH)
23 hardest BIG-Bench tasks where models initially failed.
ARC (AI2 Reasoning Challenge)
7.8K science questions testing genuine reasoning, not just recall.
hellaswag
Dataset by Rowan. 3,02,975 downloads.
RealWorldQA
Real-world visual QA requiring spatial reasoning.
HotpotQA
113K questions requiring multi-hop reasoning across Wikipedia articles.
Best For
- ✓LLM researchers evaluating model reasoning capabilities
- ✓Teams building commonsense reasoning systems
- ✓Benchmark designers seeking adversarially-robust evaluation datasets
- ✓LLM researchers running systematic model evaluations
- ✓Teams using HELM or LM Evaluation Harness for benchmark suites
- ✓Organizations tracking model quality across multiple reasoning dimensions
- ✓Researchers analyzing model reasoning capabilities in detail
- ✓Teams building commonsense-aware systems targeting specific domains
Known Limitations
- ⚠Adversarial filtering reduces dataset size compared to raw Winograd-style problems, limiting fine-tuning applications
- ⚠English-only; no multilingual variants for cross-lingual generalization testing
- ⚠Static benchmark — cannot adapt to emerging model capabilities or new failure modes without manual re-annotation
- ⚠Human performance ceiling of 94% leaves only 6% margin for superhuman evaluation
- ⚠Requires compatible evaluation harness version; older harnesses may lack WinoGrande support
- ⚠Evaluation latency scales linearly with model inference time; no built-in batching optimizations for large-scale runs
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
Large-scale commonsense reasoning benchmark with 44,000 pronoun resolution problems inspired by the original Winograd Schema Challenge. Each problem presents a sentence where a pronoun could refer to two entities, and the correct referent requires commonsense understanding. Adversarially filtered against dataset artifacts and statistical biases. Tests deep language understanding beyond surface-level pattern matching. Human performance is 94%; included in standard LLM evaluation harnesses.
Categories
Alternatives to WinoGrande
The GitHub for AI — 500K+ models, datasets, Spaces, Inference API, hub for open-source AI.
Compare →FLUX, Stable Diffusion, SDXL, SD3, LoRA, Fine Tuning, DreamBooth, Training, Automatic1111, Forge WebUI, SwarmUI, DeepFake, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, RunPod, Kaggle, NoteBooks, ControlNet, TTS, Voice Cloning, AI, AI News, ML, ML News,
Compare →Are you the builder of WinoGrande?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →