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pith:2022:TRRS5IT2GHTQ4SJS3552DBVHYS
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Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Aakanksha Chowdhery, Denny Zhou, Ed H. Chi, Hyung Won Chung, Jason Wei, Mirac Suzgun, Nathanael Sch\"arli, Nathan Scales, Quoc V. Le, Sebastian Gehrmann, Yi Tay

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

Chain-of-thought prompting enables large language models to surpass average human performance on 17 of 23 challenging BIG-Bench tasks.

References

42 extracted · 42 resolved · 15 Pith anchors

[1] Program Synthesis with Large Language Models · arXiv:2108.07732
[2] Language models are few-shot learners 1901
[3] Evaluating Large Language Models Trained on Code · arXiv:2107.03374
[4] Smith, and Tao Yu
[5] PaLM: Scaling Language Modeling with Pathways · arXiv:2204.02311

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9c632ea27a31e70e4932df7ba186a7c49aec1ad1af2f8b945a131ecc28216e5f

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arxiv: 2210.09261 · arxiv_version: 2210.09261v1 · doi: 10.48550/arxiv.2210.09261 · pith_short_12: TRRS5IT2GHTQ · pith_short_16: TRRS5IT2GHTQ4SJS · pith_short_8: TRRS5IT2
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Canonical record JSON
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