思維草圖：基於自適應認知啟發式草圖的高效大語言模型推理

Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching

March 7, 2025

作者: Simon A. Aytes, Jinheon Baek, Sung Ju Hwang

cs.AI

摘要

近期大型語言模型的進展，通過思維鏈（Chain of Thought, CoT）提示展現了卓越的推理能力，但這往往伴隨著中間輸出過於冗長的代價，從而增加了計算開銷。我們引入了思維草圖（Sketch-of-Thought, SoT），這是一種新穎的提示框架，結合了認知啟發的推理範式與語言約束，以最小化令牌使用量，同時保持推理準確性。SoT被設計為一個靈活的框架，能夠整合任何基於認知科學的自定義推理範式，我們並以三種此類範式——概念鏈接（Conceptual Chaining）、分塊符號化（Chunked Symbolism）和專家詞彙（Expert Lexicons）——來實例化它，每種範式針對不同的推理任務，並通過輕量級路由模型動態選擇。通過在15個推理數據集上進行跨語言和多模態場景的全面評估，我們證明SoT實現了76%的令牌減少，且對準確性的影響微乎其微。在某些領域，如數學和多跳推理中，它甚至在使用顯著更少令牌的同時提高了準確性。我們的代碼已公開提供：https://www.github.com/SimonAytes/SoT。

English

Recent advances in large language models have demonstrated remarkable reasoning capabilities through Chain of Thought (CoT) prompting, but often at the cost of excessive verbosity in their intermediate outputs, which increases computational overhead. We introduce Sketch-of-Thought (SoT), a novel prompting framework that combines cognitive-inspired reasoning paradigms with linguistic constraints to minimize token usage while preserving reasoning accuracy. SoT is designed as a flexible framework that can incorporate any custom reasoning paradigms based on cognitive science, and we instantiate it with three such paradigms - Conceptual Chaining, Chunked Symbolism, and Expert Lexicons - each tailored to different reasoning tasks and selected dynamically via a lightweight routing model. Through comprehensive evaluation across 15 reasoning datasets with multiple languages and multimodal scenarios, we demonstrate that SoT achieves token reductions of 76% with negligible accuracy impact. In certain domains like mathematical and multi-hop reasoning, it even improves accuracy while using significantly fewer tokens. Our code is publicly available: https://www.github.com/SimonAytes/SoT.