mlsanityData to 7 Oct 2026

Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic

arXiv 2309.13339 ↗First posted 23 Sep 2023cs.CL cs.AI cs.LGcited by 34

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially when confronted with scenarios requiring multi-step reasoning. Although large language models possess extensive knowledge, their reasoning often fails to effectively utilize this knowledge to establish a coherent thinking paradigm. These models sometimes show hallucinations as their reasoning procedures are unconstrained by logical principles. Aiming at improving the zero-shot chain-of-thought reasoning ability of large language models, we propose LoT (Logical Thoughts), a self-improvement prompting framework that leverages principles rooted in symbolic logic, particularly Reductio ad Absurdum, to systematically verify and rectify the reasoning processes step by step. Experimental evaluations conducted on language tasks in diverse domains, including arithmetic, commonsense, symbolic, causal inference, and social problems, demonstrate the efficacy of enhanced reasoning by logic. The implementation code for LoT can be accessed at: https://github.com/xf-zhao/LoT.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 22 of its 70 references that are in the map. First posted on arXiv 23 Sep 2023.

Builds on

22 of 70 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Deductive Verification of Chain-of-Thought Reasoning6 Jun
  2. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity8 Feb
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Models6 Oct
  4. 2022Faithful Reasoning Using Large Language Models30 Aug
  5. 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning19 May

Other references · 17

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  5. 2023Let's Verify Step by Stepcited by 3,598
  6. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  7. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  8. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  9. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  10. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 642
  11. 2023RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackcited by 615
  12. 2022Self-critiquing models for assisting human evaluatorscited by 372
5 more
  1. 2022Generating Sequences by Learning to Self-Correctcited by 271
  2. 2020Neurosymbolic AI: The 3rd Wavecited by 253
  3. 2022Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanationscited by 159
  4. 2021Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLPcited by 130
  5. 2021Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoningcited by 85

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 13; 2025: 17; 2026: 3.

34 papers.

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  1. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 428
  2. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  3. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  4. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 46
  5. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  6. 2024Disentangling Memory and Reasoning Ability in Large Language Modelscited by 43
  7. 2025Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedbackcited by 30
  8. 2024Meta Reasoning for Large Language Modelscited by 29
  9. 2024Can We Verify Step by Step for Incorrect Answer Detection?cited by 15
  10. 2024DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planningcited by 14
  11. 2024Large Language Models for Orchestrating Bimanual Robotscited by 10
  12. 2025Effects of Prompt Length on Domain-specific Tasks for Large Language Modelscited by 10
22 more
  1. 2025Benchmarking Multi-Step Legal Reasoning and Analyzing Chain-of-Thought Effects in Large Language Modelscited by 9
  2. 2024Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Informationcited by 8
  3. 2024KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregationcited by 7
  4. 2025LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Languagecited by 7
  5. 2025CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Gamescited by 6
  6. 2025Bridging Language Models and Financial Analysiscited by 4
  7. 2025Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learningcited by 4
  8. 2025Effect of Selection Format on LLM Performancecited by 3
  9. 2025Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neurosciencecited by 3
  10. 2025ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimizationcited by 3
  11. 2024Mental Modeling of Reinforcement Learning Agents by Language Modelscited by 2
  12. 2024Document-Level Event Extraction with Definition-Driven ICLcited by 2
  13. 2025LLM-based Interactive Imitation Learning for Robotic Manipulationcited by 2
  14. 2025PersRM-R1: Enhance Personalized Reward Modeling with Reinforcement Learningcited by 2
  15. 2025Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Modelscited by 2
  16. 2026Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs: A Systematic Evaluationcited by 1
  17. 2024An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problemscited by 0
  18. 2025Large Multi-modal Model Cartographic Map Comprehension for Textual Locality Georeferencingcited by 0
  19. 2025QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Codecited by 0
  20. 2025Knots: A Large-Scale Multi-Agent Enhanced Expert-Annotated Dataset and LLM Prompt Optimization for NOTAM Semantic Parsingcited by 0
  21. 2026Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generationcited by 0
  22. 2026BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigationcited by 0

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  7. 2022Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
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