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The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models

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arxiv 2401.05618 v3 pith:FUB2PBZW submitted 2024-01-11 cs.CL cs.AI

Matthew Renze , Erhan Guven This is my paper
classification cs.CLcs.AI
keywords ccotgpt-3averageconcisegithubgpt-4lengthperformance
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we introduce Concise Chain-of-Thought (CCoT) prompting. We compared standard CoT and CCoT prompts to see how conciseness impacts response length and correct-answer accuracy. We evaluated this using GPT-3.5 and GPT-4 with a multiple-choice question-and-answer (MCQA) benchmark. CCoT reduced average response length by 48.70% for both GPT-3.5 and GPT-4 while having a negligible impact on problem-solving performance. However, on math problems, GPT-3.5 with CCoT incurs a performance penalty of 27.69%. Overall, CCoT leads to an average per-token cost reduction of 22.67%. All code, data, and supplemental materials are available on GitHub at https://github.com/matthewrenze/jhu-concise-cot

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework

    cs.CL 2026-04 unverdicted novelty 7.0 of 10

    UL-XCoT maintains competitive accuracy on multilingual benchmarks while cutting decoding tokens by over 50% through per-query language selection and logic-space trajectory pruning.

  2. Efficient Reasoning on the Edge

    cs.LG 2026-03 accept novelty 5.5 of 10

    LoRA adapters, budget-forced GRPO, dynamic switching, parallel verification and FPTQuant enable practical chain-of-thought reasoning on quantized Qwen2.5-7B for edge devices.

  3. Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning

    cs.CL 2026-07 conditional novelty 5.0 of 10

    Warm-starting on-policy self-distillation with a correctness-filtered, prompt-switched SFT bootstrap improves compression and accuracy of reasoning traces across math benchmarks.

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