Let Your Graph Do the Talking: Encoding Structured Data for LLMs
Bryan Perozzi Bahare Fatemi Dustin Zelle Anton Tsitsulin Mehran Kazemi Rami Al-Rfou Jonathan Halcrow
How can we best encode structured data into sequential form for use in large
language models (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.