Struct-X: Enhancing Large Language Models Reasoning with Structured Data

• URL: http://arxiv.org/abs/2407.12522v1
• Date: Wed, 17 Jul 2024 13:06:25 GMT
• Title: Struct-X: Enhancing Large Language Models Reasoning with Structured Data
• Authors: Xiaoyu Tan, Haoyu Wang, Xihe Qiu, Yuan Cheng, Yinghui Xu, Wei Chu, Yuan Qi,
• Abstract summary: Struct-X operates through five key phases: read-model-fill-reflect-reason'' It encodes structured data into a topological space using graph embeddings. It fills in missing entity information with knowledge retrieval modules. The final phase involves constructing a topological network with selected tokens.
• Score: 38.558614152006975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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