mlsanityData to 14 Oct 2026

ChatLogic: Integrating Logic Programming with Large Language Models for Multi-Step Reasoning

arXiv 2407.10162 ↗First posted 14 Jul 2024cs.AIcited by 7

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Large language models (LLMs) such as ChatGPT and GPT-4 have demonstrated impressive capabilities in various generative tasks. However, their performance is often hampered by limitations in accessing and leveraging long-term memory, leading to specific vulnerabilities and biases, especially during long interactions. This paper introduces ChatLogic, an innovative framework specifically targeted at LLM reasoning tasks that can enhance the performance of LLMs in multi-step deductive reasoning tasks by integrating logic programming. In ChatLogic, the language model plays a central role, acting as a controller and participating in every system operation stage. We propose a novel method of converting logic problems into symbolic integration with an inference engine. This approach leverages large language models' situational understanding and imitation skills and uses symbolic memory to enhance multi-step deductive reasoning capabilities. Our results show that the ChatLogic framework significantly improves the multi-step reasoning capabilities of LLMs. The source code and data are available at urlhttps://github.com/Strong-AI-Lab/ChatLogic

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 22 of its 29 references that are in the map. First posted on arXiv 14 Jul 2024.

Builds on

22 of 29 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text15 Jul
  2. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning20 May
  3. 2022Towards Reasoning in Large Language Models: A Survey20 Dec
  4. 2022Faithful Reasoning Using Large Language Models30 Aug
  5. 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning19 May

Other references · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  7. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  8. 2023Teaching Large Language Models to Self-Debugcited by 988
  9. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 606
  10. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  11. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 463
  12. 2022LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Modelscited by 462
5 more
  1. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 318
  2. 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 140
  3. 2023Solving Math Word Problems by Combining Language Models With Symbolic Solverscited by 124
  4. 2022Training Language Models with Memory Augmentationcited by 113
  5. 2023Scaling Transformer to 1M tokens and beyond with RMTcited by 98

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

  1. 2025Empowering LLMs with Logical Reasoning: A Comprehensive Surveycited by 51
  2. 2025Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM Systemcited by 10
  3. 2025Conflict-Aware Fusion: Mitigating Logic Inertia in Large Language Models via Structured Cognitive Priorscited by 5
  4. 2025Improving Rule-based Reasoning in LLMs using Neurosymbolic Representationscited by 3
  5. 2025Integrating Expert Knowledge into Logical Programs via LLMscited by 3
  6. 2026Not What You Meant: Can LLMs Follow a Specified Negation Semantics?cited by 0
  7. 2026LLMs for Executable Multi-Agent System Specification Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Logical Reasoning in Large Language Models: A Survey
  2. 2025JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models
  3. 2024Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
  4. 2026Opening the Black Box: A Survey on the Mechanisms of Multi-Step Reasoning in Large Language Models
  5. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
  6. 2024A Closer Look at Logical Reasoning with LLMs: The Choice of Tool Matters
  7. 2024LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models
  8. 2025Do Large Language Models Excel in Complex Logical Reasoning with Formal Language?