Self-Harmonized Chain of Thought
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.
Paper Type: Long
Research Area: Question Answering
Research Area Keywords: Chain of Thought Prompting, Large Language Models, In-context Learning, Few-shot Learning, Arithmetic Reasoning, Commonsense Reasoning, Symbolic Reasoning
Contribution Types: NLP engineering experiment
Languages Studied: English;
Submission Number: 5808
Loading