Let Your Graph Do the Talking: Encoding Structured Data for LLMs (2402.05862v1)

Abstract: How can we best encode structured data into sequential form for use in LLMs? In this work, we introduce a parameter-efficient method to explicitly represent structured data for LLMs. Our method, GraphToken, learns an encoding function to extend prompts with explicit structured information. Unlike other work which focuses on limited domains (e.g. knowledge graph representation), our work is the first effort focused on the general encoding of structured data to be used for various reasoning tasks. We show that explicitly representing the graph structure allows significant improvements to graph reasoning tasks. Specifically, we see across the board improvements - up to 73% points - on node, edge and, graph-level tasks from the GraphQA benchmark.

• The paper introduces GraphToken as a parameter-efficient method for integrating structured data into LLM prompts.
• It leverages a learned graph prompt using GNNs to align structured representations with the LLM embedding space.
• Empirical results show performance improvements up to 73% across node, edge, and graph-level tasks on the GraphQA benchmark.

Introduction to GraphToken: A Novel Approach for Structured Data Encoding in LLMs

The integration of structured data into LLMs presents a significant challenge and opportunity for enhancing the capabilities of generative AI systems. Despite the considerable progress in LLMs, efficiently encoding structured information for general reasoning tasks remains an underexplored area. In this context, the newly proposed method, GraphToken, brings forward a promising solution aimed at bridging this gap.

GraphToken: Breaking New Ground in Structured Data Representation

GraphToken introduces a parameter-efficient encoding function specifically designed for structured data, diverging from conventional text-based serialization methods which can be inefficient and complex for LLMs to decode and utilize structured data. Unlike fixed encoding systems that limit the dynamic representation of structured data, GraphToken employs a learned graph prompt function that enriches LLM prompts with explicitly encoded structural information. This novel approach enables LLMs to preserve their reasoning and language capabilities while integrating structured data seamlessly.

Significantly, GraphToken is designed to work with a variety of graph reasoning tasks without necessitating a substantial increase in LLM parameter counts. It operates by learning soft-token prompts – a series of continuous representations directly produced from structured data via Graph Neural Networks (GNNs) – and aligning these representations with the embedding space of an LLM.

Empirical Validation and Insights

The empirical evaluation of GraphToken showcases its superior performance across an array of graph reasoning tasks from the GraphQA benchmark, demonstrating remarkable improvements – up to 73 percentage points across node, edge, and graph-level tasks. These strong numerical results underscore the effectiveness of explicitly representing structured data for graph reasoning with LLMs.

Further analysis into the choice of graph convolution and node features reveals critical insights. The performance differential among various graph encoder architectures emphasizes the importance of selecting an appropriate graph convolution method tailored to the specific characteristics of the reasoning task at hand. Moreover, the paper indicates that breaking equivariance through learned positional embeddings can enhance GraphToken’s graph reasoning capabilities, offering a novel perspective on the encoder design for structured data.

Future Directions and Conclusion

The introduction of GraphToken marks a significant advancement in the field of graph reasoning with LLMs, paving the way for future explorations in encoding structured data. Potential research directions include the design of graph convolutions optimizing LLM support, applications in factual grounding, and the reciprocal enhancement of GNNs through LLMs.

GraphToken's pioneering approach to structured data encoding in LLMs has demonstrated substantial improvements in graph reasoning tasks, providing a robust foundation for further innovation in the field. Its development not only addresses the immediate challenges of integrating structured data into LLMs but also opens up new avenues for research and application, making it a keystone contribution to the domain of generative AI.