  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
\url{https://github.com/Strong-AI-Lab/ChatLogic}
