Self-Harmonized Chain of Thought
Ziqi Jin, Wei Lu
2024-09-12
Summary
This paper talks about ECHO, a new method for improving how large language models think through problems step by step using a technique called Chain-of-Thought (CoT) prompting.
What's the problem?
While Chain-of-Thought prompting helps language models reason through complex problems by breaking them down into steps, existing methods can lead to mistakes. These methods either use simple prompts, human-made examples, or automated demonstrations, but they often struggle with consistency and effectiveness when dealing with diverse problem-solving approaches.
What's the solution?
To address these issues, the authors introduced ECHO, which combines different ways of reasoning into a single, effective pattern. This self-harmonized approach allows the model to learn from various solution paths while maintaining clarity and accuracy in its reasoning process. As a result, ECHO improves the model's performance across multiple reasoning tasks.
Why it matters?
This research is important because it enhances the ability of language models to reason effectively and accurately. By improving how these models process information step by step, ECHO can lead to better outcomes in applications like education, problem-solving tools, and AI-driven decision-making.
Abstract
Chain-of-Thought (CoT) prompting reveals that large language models are capable of performing complex reasoning via intermediate steps. CoT prompting is primarily categorized into three approaches. The first approach utilizes straightforward prompts like ``Let's think step by step'' to generate a sequential thought process before yielding an answer. The second approach makes use of human-crafted, step-by-step demonstrations to guide the model's reasoning process. The third automates the generation of reasoned demonstrations with the 'Let's think step by step'.This approach sometimes leads to reasoning errors, highlighting the need to diversify demonstrations to mitigate its misleading effects. However, diverse demonstrations pose challenges for effective representations. In this work, we propose ECHO, a self-harmonized chain-of-thought prompting method. It consolidates diverse solution paths into a uniform and effective solution pattern.ECHO demonstrates the best overall performance across three reasoning domains.