[KongchangAI]
BenchmarkBBH

BIG-Bench Hard

BIG-Bench Hard is a set of 23 of the most challenging tasks selected from the BIG-Bench benchmark, used to evaluate the complex reasoning capabilities of large language models.

Timeline (last 90 days)

Oct 20

Traditional judgment models output the final answer directly, skipping intermediate reasoning steps, and have limited accuracy on complex tasks such as BIG-Bench Hard.

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Oct 20

BIG-Bench Hard is a subset of 23 high-difficulty tasks selected from the BIG-Bench benchmark, covering types such as multi-step arithmetic, logical reasoning, causal judgment, and date understanding.

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Oct 20

On three selected BIG-Bench Hard tasks, nine independently constructed programs solved 450 executions of 150 development inputs.

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Oct 20

ABACUS programs achieve 99.67% macro-average accuracy on the three BIG-Bench Hard tasks.

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Oct 14

On Llama-3.2-3B, fine-tuning a brand-new dense model with the 5% of data selected by CACTUS reached 49.44% accuracy on BIG-Bench Hard; selecting data directly with the dense model gave 49.54%; data selected by the ALPS method could only reach 47.22%.

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Aug 29

评估大语言模型推理可靠性的系统化基准测试包括GSM8K、MATH竞赛题集和BIG-Bench Hard

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