Sketch-of-Thought: Efficient LLM Reasoning with Adaptive
  Cognitive-Inspired Sketching
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.
67ce4bff5847e4787a7ebedd
Simon A. Aytes
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2025-03-10T08:01:10.363Z
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Simon Aytes
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saytes
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Jinheon Baek
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2025-03-10T08:01:13.328Z
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Jinheon Baek
user
jinheon
67ce4bff5847e4787a7ebedf
Sung Ju Hwang
Sketch-of-Thought: Efficient LLM Reasoning with Adaptive
  Cognitive-Inspired Sketching
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.
A prompting framework named Sketch-of-Thought combines cognitive reasoning paradigms to reduce token usage in large language models with minimal impact on accuracy.
Chain of Thought Sketch-of-Thought Conceptual Chaining Chunked Symbolism Expert Lexicons prompting framework cognitive-inspired reasoning reasoning datasets multimodal scenarios mathematical reasoning multi-hop reasoning