  Structured data, rich in logical and relational information, has the
potential to enhance the reasoning abilities of large language models (LLMs).
Still, its integration poses a challenge due to the risk of overwhelming LLMs
with excessive tokens and irrelevant context information. To address this, we
propose Struct-X, a novel framework that operates through five key phases:
``read-model-fill-reflect-reason'' efficiently enabling LLMs to utilize
structured data. It begins by encoding structured data into a topological space
using graph embeddings, followed by filling in missing entity information with
knowledge retrieval modules, and filtering out irrelevant tokens via a
self-supervised module. The final phase involves constructing a topological
network with selected tokens to further reduce the total token length for more
effective LLM inference. Additionally, Struct-X includes an Auxiliary Module
trained to generate prompts, aiding LLMs in analyzing structured data.
Extensive experiments on benchmarks, including the knowledge graph
question-answer task and the long document reading comprehension task, show
that Struct-X notably improves LLM reasoning, demonstrating the effectiveness
of structured data augmentation in improving LLM inference with complex input
context.
