The content discusses the limitations of Large Language Models (LLMs) in reasoning due to their autoregressive architecture, proposing different techniques to enhance their reasoning capabilities. One surprising approach is using Prolog as an intermediate language to aid LLMs in generating accurate code for solving problems. The “Reliable Reasoning Beyond Natural Language” paper introduces a neurosymbolic approach, converting user requests into Prolog code for improved reasoning. The ProSLM paper explores explainable domain-specific question-answering by utilizing Prolog’s backward chaining method. The new datasets like NLR demonstrate significant improvements in problem-solving when combining GPT4 with Prolog.
https://shchegrikovich.substack.com/p/use-prolog-to-improve-llms-reasoning