CHAIN-OF-THOUGHT PROMPTING ELICITS REASONING IN LARGE LANGUAGE MODELS

Abstract

“Chain-of-Thought Prompting Elicits Reasoning in Large Language Models” artificial intelligence project explore a novel prompting strategy that enhances the reasoning capabilities of large language models (LLMs). Chain-of-thought (CoT) prompting involves providing explicit intermediate reasoning steps within the prompt, enabling the model to break down complex tasks into smaller, manageable sub-tasks. This approach significantly improves the model’s performance on reasoning-intensive tasks such as arithmetic, logical deduction, and commonsense reasoning. By fostering a step-by-step problem-solving approach, CoT prompting demonstrates that LLMs can effectively simulate reasoning patterns, bridging the gap between language understanding and decision-making.

Introduction

Large Language Models (LLMs) like GPT and similar architectures have shown remarkable abilities in generating coherent text and solving straightforward tasks. However, their performance on reasoning-intensive challenges remains inconsistent, particularly when tasks require multi-step problem-solving.

The Chain-of-Thought (CoT) Prompting strategy introduces an innovative approach to improve reasoning capabilities by guiding LLMs to think in steps, rather than delivering direct answers. This strategy provides explicit examples of reasoning processes, encouraging the model to emulate similar logical steps. CoT prompting not only enhances the accuracy of responses but also makes the decision-making process interpretable, which is crucial for trust and usability in applications requiring reasoning.

Existing System

1. Direct Answer Prompting:
   • LLMs often generate single-shot responses to complex queries.
   • Limited reasoning capacity, leading to errors in multi-step problems.
2. Zero-Shot and Few-Shot Learning:
   • Using context or a few examples improves performance but lacks explicit reasoning guidance.
   • Struggles with tasks requiring structured thought processes.
3. Rule-Based Reasoning:
   • External symbolic systems excel at logical tasks but lack the generalization capabilities of LLMs.

Proposed System

Chain-of-Thought Prompting aims to address the reasoning limitations by:

• Guided Reasoning: Embedding step-by-step examples within the prompt to guide the model.
• Generalization Across Tasks: Demonstrating improvements in diverse reasoning challenges such as math word problems, logical puzzles, and scientific reasoning.
• Enhanced Interpretability: Making the intermediate steps visible, thereby enabling users to understand how the model arrived at its conclusions.

Methodology

1. Dataset Preparation:
   • Collect datasets with reasoning tasks, including benchmarks like GSM8K (math problems), CommonsenseQA, and others.
2. Prompt Design:
   • Create chain-of-thought exemplars for the tasks by including intermediate reasoning steps explicitly in the prompt.
   Example Prompt

Q: If there are 3 apples and you take away 2, how many do you have?
A: Let’s think step by step.
Step 1 There are 3 apples initially.
2 You take away 2 apples.
3 You now have 2 apples.
Final Answer: 2 apples.

1. Evaluation:
   • Test model performance with and without CoT prompts.
   • Compare accuracy and consistency on various reasoning tasks.
2. Analysis:
   • Examine how the inclusion of CoT prompts influences the reasoning depth and accuracy.

Technologies Used

1. Language Models: OpenAI GPT, Google PaLM, or other advanced LLMs.
2. Programming Language: Python.
3. Machine Learning Frameworks: TensorFlow, PyTorch.
4. Evaluation Metrics:
   • Task-specific metrics (e.g., accuracy, BLEU, ROUGE).
   • Logical consistency and interpretability assessments.