RAG-Logic is a dynamic example-based framework that enhances logical reasoning by integrating contextually appropriate examples using a four-step process: (1) encoding symbolic input into neural representations via a RAG knowledge base search module, (2) transforming input into formal logical formulas via a translation module, (3) ensuring syntactic correctness via a fix module, and (4) evaluating logical consistency via a solver module before decoding results back into symbolic output.

Authors

• Person: Not available Organization: arXiv
  Unlocking the Potential of Generative AI through Neuro-Symbolic ...

Sources

• Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv via serper

Referenced by nodes (1)

• logical reasoning concept