@inproceedings{liao-etal-2025-forest,
    title = "Forest for the Trees: Overarching Prompting Evokes High-Level Reasoning in Large Language Models",
    author = "Liao, Haoran  and
      Hu, Shaohua  and
      Zhu, Zhihao  and
      He, Hao  and
      Jin, Yaohui",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.naacl-long.66/",
    pages = "1433--1453",
    ISBN = "979-8-89176-189-6",
    abstract = "Chain-of-thought (CoT) and subsequent methods adopted a deductive paradigm that decomposes the reasoning process, demonstrating remarkable performances across NLP tasks. However, such a paradigm faces the challenge of getting bogged down in low-level semantic details, hindering large language models (LLMs) from correctly understanding, selecting, and compositing conditions. In this work, we present Overarching Prompting (OaP), a simple prompting method that elicits the high-level thinking of LLMs. Specifically, OaP first abstracts the whole problem into a simplified archetype and formulates strategies grounded in concepts and principles, establishing an overarching perspective for guiding reasoning. We conducted experiments with SoTA models, including ChatGPT, InstructGPT, and Llama3-70B-instruct, and received promising performances across tasks including Knowledge QA, Mathematical, and Open-Domain Reasoning. For instance, OaP improved ChatGPT and CoT by 19.0{\%} and 3.1{\%} on MMLU{'}s College Physics, 8.8{\%} and 2.3{\%} on GSM8k, and 10.3{\%} and 2.5{\%} on StrategyQA, respectively."
}