pith:TRRS5IT2
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Chain-of-thought prompting lets current models surpass average humans on 17 of 23 BIG-Bench Hard tasks.
arxiv:2210.09261 v1 · 2022-10-17 · cs.CL · cs.AI
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Claims
Applying chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the average human-rater performance on 10 of the 23 tasks, and Codex (code-davinci-002) to surpass the average human-rater performance on 17 of the 23 tasks.
That the average human-rater scores reported in the original BIG-Bench paper constitute a stable and fair external benchmark against which model performance can be directly compared without additional controls for prompt sensitivity or rater variability.
Chain-of-thought prompting enables large language models to surpass average human performance on 17 of 23 challenging BIG-Bench tasks.
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| First computed | 2026-07-05T05:07:20.597704Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/TRRS5IT2GHTQ4SJS3552DBVHYS \
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# expect: 9c632ea27a31e70e4932df7ba186a7c49aec1ad1af2f8b945a131ecc28216e5f
Canonical record JSON
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