Struct-X: Enhancing Large Language Models Reasoning with Structured Data
<|reference_start|>Struct-X: Enhancing Large Language Models Reasoning with Structured Data: 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.<|reference_end|>
@article{tan2024struct-x:,
    title={Struct-X: Enhancing Large Language Models Reasoning with Structured Data},
    author={Xiaoyu Tan, Haoyu Wang, Xihe Qiu, Yuan Cheng, Yinghui Xu, Wei Chu,
  Yuan Qi},
    journal={arXiv preprint arXiv:2407.12522},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12522},
    primaryClass={cs.CL cs.AI}
}