By decoupling graph traversal from text generation and using soft probability flows that converge to discrete paths, the approach enables end-to-end learning across semantic gaps while maintaining computational efficiency compared to pure LLM methods.
This paper tackles multi-hop question answering over knowledge graphs by proposing RSF-GLLM, which separates differentiable graph reasoning from LLM-based answer generation. A Recurrent Soft-Flow module learns to traverse semantically distant nodes in knowledge graphs by propagating relevance scores, then converts discovered paths into text to fine-tune an LLM for grounded answers.