BIG-Bench Hard
BenchmarkFreeSubset of BIG-Bench where most models fail
Capabilities3 decomposed
reasoning capability evaluation
Medium confidenceBIG-Bench Hard evaluates the reasoning capabilities of language models by utilizing a curated subset of tasks that specifically challenge models on their reasoning limits rather than their memorization skills. It employs a systematic approach to select tasks where models have historically underperformed compared to task-specific baselines, ensuring a rigorous assessment of true reasoning abilities. This focus on capability boundaries distinguishes it from other benchmarks that may not emphasize reasoning as heavily.
The curation of tasks specifically targeting reasoning limits rather than general performance allows for a more focused evaluation of model capabilities.
More targeted than generic benchmarks, as it specifically identifies and tests reasoning weaknesses in models.
task-specific baseline comparison
Medium confidenceThis capability allows users to compare model performance against established task-specific baselines, providing a clear metric for evaluating reasoning abilities. By leveraging a set of predefined benchmarks, it systematically measures how well a language model performs relative to these baselines, enabling users to identify specific areas of improvement. This structured comparison is essential for understanding the limitations of current models in reasoning tasks.
Utilizes a curated set of benchmarks that focus on reasoning tasks, providing a more relevant comparison than general performance metrics.
Offers a more nuanced view of model performance by focusing specifically on reasoning-related tasks, unlike broader benchmarks.
capability boundary identification
Medium confidenceBIG-Bench Hard is designed to identify the capability boundaries of language models by focusing on tasks where they have historically underperformed. This is achieved through a careful selection process that emphasizes tasks that challenge reasoning skills, allowing researchers to pinpoint where models fail to meet expectations. This capability is crucial for advancing AI research by revealing the limits of current technologies.
The focus on identifying underperformance in reasoning tasks allows for a targeted approach to understanding model limitations, which is not common in other benchmarks.
Provides a clearer view of reasoning capabilities compared to broader benchmarks that do not focus on specific weaknesses.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of lang... (BIG-bench)
* ⭐ 06/2022: [Solving Quantitative Reasoning Problems with Language Models (Minerva)](https://arxiv.org/abs/2206.14858)
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.
LiveBench
Continuously updated contamination-free LLM benchmark.
Artificial Analysis
Artificial Analysis provides objective benchmarks & information to help choose AI models and hosting providers.
Exploiting the most prominent AI agent benchmarks
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Best For
- ✓researchers testing AI models for reasoning capabilities
- ✓developers improving AI performance on complex tasks
- ✓data scientists analyzing model performance
- ✓AI developers seeking to improve reasoning tasks
- ✓AI researchers exploring model limitations
- ✓developers enhancing AI reasoning capabilities
Known Limitations
- ⚠Limited to 23 tasks, which may not cover all reasoning scenarios
- ⚠Focuses only on tasks where models performed worse than baselines, potentially excluding easier tasks
- ⚠Requires access to baseline performance data
- ⚠May not account for all variables affecting model performance
- ⚠Limited to the selected 23 tasks, which may not represent all reasoning scenarios
- ⚠Focus on underperformance may overlook potential strengths
Requirements
Input / Output
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UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
BBH is a carefully curated 23-task subset of the original 200+ BIG-Bench tasks. Focuses only on tasks where language models performed worse than task-specific baselines. Tests true reasoning limits rather than memorized patterns. Good for finding capability boundaries.
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