Pith. sign in

REVIEW 4 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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
0 comments
read the original abstract

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

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

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

  1. Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning

    cs.AI 2026-08 conditional novelty 6.0 of 10

    ReCo coordinates KV-cache compression, reflection-token logit penalties, and confidence-based early stopping under one per-step process reward, reducing tokens and latency while largely preserving accuracy.

  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.

  4. Incorporating Token Usage into Prompting Strategy Evaluation

    cs.CL 2025-05 conditional novelty 4.0 of 10

    Prompting strategies show sharply diminishing accuracy returns as token usage increases, and the paper formalizes this with Big-O_tok and Token Cost metrics.

Pith tools