Pith Number

pith:OOIW6D7P

pith:2022:OOIW6D7P2P6SYAWF6IWLW6ZI44

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Automatic Chain of Thought Prompting in Large Language Models

Alex Smola, Aston Zhang, Mu Li, Zhuosheng Zhang

Auto-CoT lets large language models build their own chain-of-thought demonstrations by sampling diverse questions.

arxiv:2210.03493 v1 · 2022-10-07 · cs.CL · cs.AI

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

On ten public benchmark reasoning tasks with GPT-3, Auto-CoT consistently matches or exceeds the performance of the CoT paradigm that requires manual designs of demonstrations.

C2weakest assumption

That sampling questions for diversity sufficiently mitigates the impact of occasional errors in the automatically generated reasoning chains, so that the constructed demonstrations remain effective overall.

C3one line summary

Auto-CoT automatically builds chain-of-thought demonstrations by sampling diverse questions and letting the LLM generate reasoning chains, matching manual CoT performance on ten reasoning tasks with GPT-3.

References

32 extracted · 32 resolved · 9 Pith anchors

[1] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-V oss, Gretch 2020

[2] URL https://proceedings.neurips.cc/paper/2020/hash/ 1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html. Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, A 2020

[3] LaMDA: Language Models for Dialog Applications · arXiv:2201.08239

[4] PaLM: Scaling Language Modeling with Pathways 2022 · arXiv:2204.02311

[5] Large Language Models are Zero-Shot Reasoners 2015 · arXiv:2205.11916

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Cited by

37 papers in Pith

DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition

CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging

The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation

Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data

ACIL: Auto Chain of Thoughts for In-Context Learning

Receipt and verification

First computed	2026-05-17T23:38:48.161086Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

73916f0fefd3fd2c02c5f22cbb7b28e72a6d630a8d821461cda07f7142e9b681

Aliases

arxiv: 2210.03493 · arxiv_version: 2210.03493v1 · doi: 10.48550/arxiv.2210.03493 · pith_short_12: OOIW6D7P2P6S · pith_short_16: OOIW6D7P2P6SYAWF · pith_short_8: OOIW6D7P

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OOIW6D7P2P6SYAWF6IWLW6ZI44 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 73916f0fefd3fd2c02c5f22cbb7b28e72a6d630a8d821461cda07f7142e9b681

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "899052b0b951193f4702fba551d1dad6445035f19118485fbbf21ea5a65f64be",
    "cross_cats_sorted": [
      "cs.AI"
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    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2022-10-07T12:28:21Z",
    "title_canon_sha256": "3abecc45e8f72884c7d98a88e67f227a9e9be1b2961d12479cbdf99082dd4715"
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