Neurosymbolic Retrievers for Retrieval-augmented Generation

Retrieval-augmented generation (RAG) has made significant strides in overcoming key limitations of large language models, such as hallucination, lack of contextual grounding, and issues with transparency. This new framework aims to answer two primary questions: 1) Can retrievers provide a clear and interpretable basis for document selection? 2) Can symbolic knowledge enhance the clarity of the retrieval process? We propose three methods to improve this integration. The first is modulation augmented retrieval which employs modulation networks to refine query embeddings using interpretable symbolic features, thereby making document matching more explicit. The second is KG-Path RAG, which enhances queries by traversing knowledge graphs to improve overall retrieval quality and interpretability. Finally, process knowledge-infused RAG utilizes domain-specific tools to reorder retrieved content based on validated workflows. Preliminary results from mental health risk assessment tasks indicate that this neurosymbolic approach enhances both transparency and overall performance.