mlsanityData to 7 Oct 2026

LogiCoT: Logical Chain-of-Thought Instruction-Tuning

arXiv 2305.12147 ↗First posted 20 May 2023cs.CL cs.AIcited by 31

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

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.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 24 of its 38 references that are in the map. First posted on arXiv 20 May 2023.

Builds on

24 of 38 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Teaching Small Language Models to Reason16 Dec
  2. 2022Large Language Models are Zero-Shot Reasoners24 May
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 21

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  6. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  7. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  8. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  9. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 895
  10. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
  11. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
  12. 2023Instruction Tuning with GPT-4cited by 676
9 more
  1. 2022PAL: Program-aided Language Modelscited by 635
  2. 2022Towards Reasoning in Large Language Models: A Surveycited by 593
  3. 2023Specializing Smaller Language Models towards Multi-Step Reasoningcited by 249
  4. 2022FOLIO: Natural Language Reasoning with First-Order Logiccited by 233
  5. 2023Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4cited by 202
  6. 2021Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collectionscited by 128
  7. 2022PINTO: Faithful Language Reasoning Using Prompt-Generated Rationalescited by 56
  8. 2023From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuningcited by 14
  9. 2022LogicInference: A New Dataset for Teaching Logical Inference to seq2seq Modelscited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 17; 2026: 6.

31 papers.

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  1. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  2. 2025VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Modelscited by 102
  3. 2024Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMscited by 48
  4. 2025Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Trainingcited by 42
  5. 2025Bring Reason to Vision: Understanding Perception and Reasoning through Model Mergingcited by 40
  6. 2023Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Modelscited by 35
  7. 2025The Homogenizing Effect of Large Language Models on Human Expression and Thoughtcited by 35
  8. 2024Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challengescited by 30
  9. 2023GLoRE: Evaluating Logical Reasoning of Large Language Modelscited by 13
  10. 2023MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applicationscited by 11
  11. 2025Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answerscited by 10
  12. 2025Evaluating Robustness of Large Language Models Against Multilingual Typographical Errorscited by 9
19 more
  1. 2025Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoningcited by 7
  2. 2025RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensationcited by 7
  3. 2025Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysiscited by 6
  4. 2025LogicReward: Incentivizing LLM Reasoning via Step-Wise Logical Supervisioncited by 6
  5. 2025Introspection of Thought Helps AI Agentscited by 4
  6. 2026Reasoning Models Will Sometimes Lie About Their Reasoningcited by 4
  7. 2024COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinementcited by 3
  8. 2024Hypothesis Testing Prompting Improves Deductive Reasoning in Large Language Modelscited by 2
  9. 2025Beyond BFI: The CSI for Enhanced Reliability and Validity in Evaluating LLM Personality Traitscited by 2
  10. 2025From Canonical to Complex: Benchmarking LLM Capabilities in Undergraduate Thermodynamicscited by 2
  11. 2025Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humanscited by 1
  12. 2025Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoningcited by 1
  13. 2026SCRuB: Social Concept Reasoning under Rubric-Based Evaluationcited by 1
  14. 2026From 0-Order Selection to 2-Order Judgment: Combinatorial Hardening Exposes Compositional Failures in Frontier LLMscited by 1
  15. 2025CDS: Knowledge Component-Driven Data Synthesis Guided by Cognitive Diagnosis Theorycited by 0
  16. 2025Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generationcited by 0
  17. 2026Visual Perceptual to Conceptual First-Order Rule Learning Networkscited by 0
  18. 2026Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understandingcited by 0
  19. 2026HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answeringcited by 0

Similar, not cited

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  1. 2023Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
  2. 2026Transformers Provably Learn to Internalize Chain-of-Thought
  3. 2024Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs
  4. 2025SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought
  5. 2025From Reasoning to Super-Intelligence: A Search-Theoretic Perspective
  6. 2024Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis
  7. 2023Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
  8. 2022Large Language Models Are Reasoning Teachers