Forest for the Trees: Overarching Prompting Evokes High-Level Reasoning in Large Language Models (2025)

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.

## introduction recent astonishing advancements in large language models (llms), including gpt @xcite , llama @xcite , mistral @xcite family models, have greatly propelled the progress of natural language process (nlp). scaling up model size and training corpus has yielded various emergent abilities @xcite in llms, such as instruction following @xcite @xcite and multi-step reasoning @xcite @xcite . among these remarkable abilities, chain of thought (cot) @xcite specifically enhances llms' multi-step reasoning performances on complex tasks by prompting models to follow a bottom-up thinking paradigm. instead of directly answering the question, cot deductively constructs intermediate rationales and utilizes them to update premises, until reaching the desired result. however, recent studies have shown that such a deductive paradigm, which relies directly on lowlevel details, exhibits failure modes that similar to human-like cognitive biases @xcite @xcite . for instance, llms can easily be misled as being extremely sensitive to irrelevant context @xcite , minor disturbances @xcite and premise order @xcite . despite their knowledge, llms can still struggle to parse the problem to acquire what is required @xcite , or which conditions should be combined @xcite . additionally, the multistep process could fail by either missing necessary steps @xcite or accumulating hallucination @xcite during rationale generation. intuitively, diving immediately into intricate details can be inefficient in tackling complex problems. as the saying goes, can't see the forest for the trees, humans tend to abstract and simplify the problem, focusing on its essence and the key to its solution at a high level. compared to handling nested and ambiguous low-level details, adopting a global perspective could improve the precision of semantics and evoke the inherent knowledge of llms about concepts and principles. abstraction could also simplify problem parsing and provide global planning, guiding the subsequent reasoning. abstract thinking as an idea has been explored in various domains including mathematics, psychology, and computer science, and is considered crucial to human intelligence @xcite @xcite . recent studies have implicitly adopted this idea to investigate how to transcend the vanilla deductive paradigm in llms. some works introduced topdown perspectives in reasoning via problem decomposition @xcite @xcite , while others examined inductive skills from raw context @xcite . however, a clear and systematic mechanism for enabling models to think abstractly still remains unexplored. these methods often require high-level inferences directly from raw context, which can produce content and patterns that significantly differ from human approaches @xcite . in this work, we introduce a novel prompting method called overarching prompting (oap) to stimulate high-level thinking in llms. in specific, we start by abstracting the entire problem context to form a problem archetype. this process is similar to compression or filtration, but it emphasizes conceptualizing and transcending low-level details to distill the problem's essence using precise semantics. the archetype further allows llms to concentrate on concepts, rules and global relations rather than locally nested details, thereby eliciting inherent advanced knowledge and forming highlevel strategies for subsequent reasoning process. we empirically validated our approach on a variety of datasets, including three knowledge qa datasets from mmlu, two mathematical reasoning datasets, and three open-domain reasoning datasets. through the experiments, we observed that the proposed method consistently outperforms baselines across various tasks. comprehensive analyses are further conducted to investigate the enhancements of oap, examining the behaviors and failure modes of llms when applying abstract thinking. while generating archetype and strategies were less prone to produce errors, oap sometimes can lead to ineffective analysis or overgeneralization errors. ## overarching prompting abstract thinking singles out the rational, logical qualities of a given content from its intellectually irrelevant components. -carl gustav jung our intuition stems from the observation that the deductive paradigm of cot may be overly detailoriented and lacks a broader perspective. by abstracting the problem into an archetype, we can enhance semantic precision, simplify the problem's parsing, and evoke llms' intrinsic knowledge. two examples are shown in fig. 1 . in multi-step mathematical tasks, the deductive process of cot focuses on capturing local relationships for reasoning. this narrow focus can lead to cognitive errors, such as incorrect composition order @xcite . in contrast, oap emphasizes abstracting details to form a clearer archetype, helping to avoid misguidance. for knowledge intensive tasks, like physics problems, reasoning directly from low-level details can hinder access to the correct knowledge. by abstracting and conceptualizing the problem into a high-level semantic archetype, the context becomes clearer and more independent, assisting llms in connecting to the correct principles and concepts. our work is inspired by recent studies that also embrace the idea of abstraction. for example, decomposing methods @xcite @xcite dissect the final question from a top-down perspective, while the step-back method @xcite prompts llms to identify required concepts or principles. they typically derive advanced strategies or actions directly from contextual details. in contrast, oap initially transforms the entire problem by considering semantic layers to promote abstract thinking. ## conclusion in this study, we present a simple method, overarching prompting (oap), to elicit high-level thinking in llms, investigating the idea of applying abstraction across reasoning tasks. oap involves two key steps: archetype generation and strategy formulation. experiments showed that oap excels on most datasets, aiding in accurately pinpointing essentials of the problems and reducing details' distractions.