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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
classification cs.CLcs.AI
keywords reasoningchain-of-thoughtgeneralgpt-4logicallogicotinstruction-tuninginstructions
verification ladder T0 review T1 audit T2 compute T3 formal
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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 2 Pith papers

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

  1. 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.

  2. 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.

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