ツリーオブソートプロンプト戦略はチェーンオブソートよりも優れているか？大規模言語モデルを使用したベイピング中止分析

Vapingis gainingpopularity amongadolescents andposes severe risks to users. Social media platforms such as Reddit and X offer insights into user behaviors and attitudes regarding vaping. In previous studies, our team explored the ability of large language models (LLMs) to perform binary classification at a sentence level to determine if LLMs can be used to identify users for a vaping cessation application. Maintaining the same goal, this study expands to compare OpenAI's GPT-o1 and GPT-o3-mini, Google's Gemini 2.0 Flash and Gemma 2, Meta's LLAMA 3.3, Deepseek's R1, and xAI's Grok-2 against human annotators to examine which models best perform binary classification to identify quit intention and multiclass classification to detect quit stages. We tested these models with emerging chain-of-thought and tree-of-thought prompts together with a simple prompt to see which strategy performed best. To our knowledge, this is the first investigation of tree-of-thought prompting. Our initial results indicate that tree-of-thought and chain-of-thought prompting do not boost performance.