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LogiCoT: Logical Chain-of-Thought Instruction-Tuning

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arxiv 2305.12147 v2 pith:ZWMMBYEW submitted 2023-05-20 cs.CL cs.AI

Hanmeng Liu , Zhiyang Teng , Leyang Cui , Chaoli Zhang , Qiji Zhou , Yue Zhang This is my paper · ORCID
classification cs.CLcs.AI
keywords reasoningchain-of-thoughtgeneralgpt-4logicallogicotinstruction-tuninginstructions
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Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models. These instructions enable the model to achieve performance comparable to GPT-3.5 on general tasks like open-domain text generation and paraphrasing. However, they fall short of helping the model handle complex reasoning tasks. To bridge the gap, this paper presents LogiCoT, a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4. We elaborate on the process of harvesting instructions for prompting GPT-4 to generate chain-of-thought rationales. LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.

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Cited by 5 Pith papers

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

  1. Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans

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    A new bilingual benchmark with per-question human accuracy and common mistakes shows current multimodal AI models still underperform humans on reasoning.

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    On VisuLogic's 1,000 vision-centric puzzles, the best multimodal models reach 28.1% accuracy versus a 24.9% random baseline and 51.4% human accuracy, and an RL baseline lifts accuracy by up to 5.6 points.

  3. Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding

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    CoT data curated by two-round LLM prompting and VLM verification, then SFT+GRPO with fine-grained rewards, improves MapDR rule–lane association F1 from 0.642 to 0.723.

  4. Introspection of Thought Helps AI Agents

    cs.AI 2025-07 conditional novelty 4.0 of 10

    INoT wraps prompts in XML-defined pseudo-code so an LLM simulates two debating agents internally, reporting better scores and lower tokens than seven baselines.

  5. Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers

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    On the GPQA benchmark, GPT-4o's highest accuracy came from self-consistency prompting, about 53 percent correct, but its explanations were least similar to the reference solutions, while direct answer and chain-of-tho...

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