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[Reasoning]·PAP-LRKYVF·March 17, 2026·★ Essential·Free Preview

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Jason Wei, Xuezhi Wang, Dale Schuurmans et al.

REASONING

4 min readReasoning

Core Insight

Chain-of-Thought Prompting elevates reasoning in LLMs, outperforming finetuned GPT-3 on complex math tasks.

By the Numbers

540B

parameters in PaLM model

examples required for effective prompting

GSM8K

benchmark where PaLM excelled

outperformed finetuned GPT-3

comparison on complex math tasks

In Plain English

The paper demonstrates 'Chain-of-Thought' prompting, improving large language models' reasoning via intermediate steps. A PaLM 540B model excelled on GSM8K math benchmarks, even besting finetuned GPT-3 with a verifier.

Knowledge Prerequisites

git blame for knowledge

To fully understand Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, trace this dependency chain first. Papers in our library are linked — click to read them.

DIRECT PREREQIN LIBRARY

Attention Is All You Need

Understanding how transformers function is crucial for appreciating reasoning capabilities in large language models.

attention mechanismtransformer architectureself-attention

DIRECT PREREQIN LIBRARY

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

This paper introduces early advancements in transformer-based language models, foundational for understanding how large models evolve reasoning skills.

bidirectional transformersmasked language modelingtransfer learning

DIRECT PREREQIN LIBRARY

Language Models are Few-Shot Learners

Understanding few-shot learning helps grasp how large language models can adapt their reasoning processes without extensive training.

few-shot learningin-context learningprompting

DIRECT PREREQIN LIBRARY

Training Compute-Optimal Large Language Models

This paper discusses the methods to efficiently train large models, which directly affects their ability to perform complex reasoning tasks.

compute efficiencymodel scalingtraining optimization

DIRECT PREREQIN LIBRARY

Self-Consistency Improves Chain of Thought Reasoning in Language Models

The concept of self-consistency is a refinement of existing chain-of-thought methods, critical for an advanced understanding of reasoning improvements.

self-consistencychain of thoughtreasoning enhancement

YOU ARE HERE

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

The Idea Graph

⚠Problem✦Insight⬡Method◎Result→Impact

12 nodes · 13 edges

Click a node to explore · Drag to pan · Scroll to zoom

419 words · 3 min read7 sections · 12 concepts

The Problem: Lack of Complex Reasoning in Models

78 words

Large language models, despite their impressive capabilities, have struggled with tasks that require complex reasoning. This limitation has been particularly evident in problems like math word problems, where models often fail to provide accurate solutions. The '' in these models highlights a significant gap in AI's ability to process and solve intricate tasks effectively. Existing approaches have not sufficiently addressed this issue, calling for new methods to unlock the latent reasoning potential of these large models.

Key Insight: The Power of Prompting

62 words

The critical insight presented in this paper is the '', which involves guiding models through structured reasoning steps. This approach leverages the '' of large models, where their ability to reason becomes more apparent when prompted effectively. By rethinking how we interact with these models, researchers have identified a way to enhance their natural reasoning abilities, which was previously untapped.

Method: Chain-of-Thought Prompting

60 words

The '' method involves using prompts that guide models step-by-step through a reasoning process. By presenting '', the model is able to break down complex tasks into manageable components, improving its ability to reason through problems. A '', consisting of just eight examples, can dramatically transform a model's reasoning capabilities, revealing the potential that was previously obscured.

Method: Application on the PaLM 540B Model

51 words

The '' is a large language model that benefited significantly from chain-of-thought prompting. By applying these methods, the model demonstrated remarkable performance improvements, showcasing its ability to handle complex reasoning tasks. This section explores how the combination of effective prompting and the model's inherent capabilities led to these breakthroughs.

Results: Outperforming on GSM8K Benchmarks

56 words

The experiments demonstrated that the 'PaLM 540B Model' outperformed existing state-of-the-art methods on the '', a collection of challenging math word problems. Notably, this model even surpassed the '', highlighting the effectiveness of the proposed prompting techniques. These results underscore the potential of structured prompting to elevate model performance on tasks requiring advanced reasoning.

Impact: A Paradigm Shift in Model Training

54 words

The success of this approach signifies a '' in how we train and prompt language models for reasoning tasks. By prioritizing both scale and strategic prompting, researchers can unlock new potentials in AI systems. This shift encourages a reevaluation of existing methodologies, promoting techniques that can better harness the capabilities of large models.

Impact: Enabling Advanced AI Applications

58 words

The development of chain-of-thought prompting opens new possibilities for '' that require advanced reasoning. Applications such as complex Q&A systems, tutoring platforms, and decision support tools stand to benefit significantly. Additionally, this approach can lead to an '', where AI becomes a more effective collaborator in tasks that demand nuanced understanding, reshaping our interactions with technology.

Experience It

Live Experiment

Chain-of-Thought Prompting

See Chain-of-Thought in Action

Wei et al. showed that asking a model to "think step by step" dramatically improves reasoning on math, logic, and common-sense problems. Enter any puzzle and see the difference yourself.

Notice the direct answer often triggers the intuitive (wrong) answer. Step-by-step reasoning forces the model — and you — to catch the error. Wei et al. showed this works at scale across dozens of reasoning benchmarks.

Try an example — see the difference instantly

Your reasoning problem — or try your own

⌘↵ to run

Read Original Paper on arXiv

Origin Story

arXiv preprintGoogle BrainJason Wei, Dale Schuurmans et al.

The Room

A group of researchers at Google Brain, 2022. They are driven by an itch — the nagging feeling that despite the massive scale of models like GPT-3, something is still missing. The room buzzes with the murmur of keyboards and the rustle of papers — minds wrestling with the limitations of logical reasoning in these otherwise powerful models.

The Bet

Instead of more training data or tweaks to architecture, they bet on a novel idea: using the model's own language to guide its reasoning. A simple yet daring contrarian move. There were moments of doubt, especially when some results seemed counterintuitive, but they held fast, believing in the potential of prompting itself to unlock untapped reasoning ability.

The Blast Radius

Without this work, models like PaLM and GPT-4 might have remained limited in reasoning, unable to tackle complex tasks with the same efficacy. The reverberations have been profound, influencing how researchers think about prompting and reasoning in AI. Key authors have continued to explore this frontier, pushing the boundaries of what language models can achieve and inspiring a new generation of research.

↳PaLM↳GPT-4↳Claude AI

Explained Through an Analogy

“

Imagine teaching someone to cook not with a full recipe but with each little step—a pinch of salt here, a stir there—only to see them perfect a gourmet dish. That's chain-of-thought prompting for language models, letting them illuminate paths through reasoning as a chef navigates flavors.

The Full Story

~1 min · 213 words

The Context

What problem were they solving?

hain-of-thought prompting involves breaking down a question into logical steps so the model can better understand and solve it.

The Breakthrough

What did they actually do?

The scaling property of language models is key; larger models show emergent improved reasoning abilities.

Under the Hood

How does it work?

By using just a few thoughtful examples, model performance can surpass that of more complex finetuned models.

World & Industry Impact

This development opens new horizons for AI applications requiring advanced reasoning, like complex Q&A, tutoring systems, and decision support tools. Companies like Google, Microsoft, or startups innovating in AI-driven education could directly integrate chain-of-thought prompting to improve model accuracy and interpretability. This approach can redefine how we interact with AI, making them more effective collaborators in tasks demanding nuanced understanding.

Highlighted Passages

Verbatim lines from the paper — the sentences that carry the most weight.

“Chain-of-thought prompting involves guiding large language models through a series of reasoning steps, rather than asking for a direct answer.”
→ This highlights the importance of structured reasoning, which is crucial for product teams designing AI systems requiring deep interpretative skills.

“What sets this apart is its operational simplicity: just eight examples can transform the model's reasoning prowess.”
→ This operational efficiency can significantly reduce development time and resources for PMs aiming to implement advanced AI functionalities.

“Key results include a PaLM 540B model outperforming existing state-of-the-art methods on GSM8K math word problems.”
→ This achievement underlines the competitive edge possible through strategic prompting, essential for PMs aiming to achieve breakthrough product performance.

Interactive Diagram

Chain-of-Thought Prompting

Step 1 / 5

Traditional Prompting Challenges

✗Direct Answer

·Limited reasoning
·Poor complex task performance

✓Chain-of-Thought

·Enhanced reasoning
·Better complex task performance

Before, large language models were asked to provide direct answers, often resulting in poor reasoning on complex tasks.

Traditional Prompting Challenges → The Insight → Prompting Mechanics → Performance Gains → Broader Implications

TL;DR

Chain-of-Thought prompting enhances large language models' reasoning by guiding them through intermediate steps, outperforming traditional methods.

Key Terms

Chain-of-Thought Prompting

A method that guides models through reasoning steps to improve performance.

It's like showing your work in math class instead of jumping to the answer.

PaLM 540B

A large language model used in this research to test Chain-of-Thought prompting.

GSM8K

A benchmark for evaluating math problem-solving capabilities of models.

Finetuned GPT-3

A version of the GPT-3 model that has been adjusted for specific tasks.

Reasoning Capabilities

The ability of a model to solve complex tasks using logical steps.

Intermediate Steps

Smaller reasoning tasks used to guide models to a final solution.

Structured Prompting

Organizing prompts in a way that guides models through logical steps.

Scaling Property

The tendency of models to improve performance as they grow in size.

Core Ideas

1
Structured Prompting
It transforms a model's ability to reason by guiding it through logical steps.
2
Model Scaling
Larger models reveal more complex reasoning abilities through Chain-of-Thought prompting.
3
Benchmark Excellence
Achieves superior performance on complex math tasks compared to traditional methods.
4
Operational Simplicity
Demonstrates that a few structured examples can significantly enhance model reasoning.

Key Formula

Performance = Scale × Structure

Performance

The model's ability to solve complex tasks.

Scale

The size of the model.

Structure

The method of prompting, such as Chain-of-Thought.

Before vs After

Before

Models struggled with complex reasoning tasks, often providing incorrect or incomplete solutions.

After

Models equipped with Chain-of-Thought prompting excel in reasoning, outperforming even finetuned versions like GPT-3.

Remember it as

"Think of Chain-of-Thought prompting as giving the model a roadmap to reason its way to the answer."

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Source Richness100%

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Source Depth~223 words

Total source text analyzed by the model. Includes extended deep-dive summary — high confidence.

Number Grounding4 / 4

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Quote Traceability3 / 3

Key passages whose significant vocabulary (≥4-char words) overlap ≥35% with source text. Measures lexical traceability, not semantic accuracy.

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