FoGE: Fock Space inspired encoding for graph prompting

Recent results show that modern Large Language Models (LLM) are capable of understanding and answering questions about structured data such as graphs. Existing proposals often use some description of the graph to create an "augmented" prompt fed to the LLM. For a chosen class of graphs, if a well-tailored graph encoder is deployed to play together with a pre-trained LLM, the model can answer graph-related questions well. Current solutions to graph-based prompts range from graph serialization to graph transformers. In this work, we show that the use of a parameter-free graph encoder based on Fock space representations, a concept borrowed from physics, is remarkably versatile in this problem setting. The simple construction, with a few small adjustments, can provide rich and informative graph encodings, for a wide range of different graphs. We investigate the use of this idea for prefix-tuned prompts leveraging the capabilities of a pre-trained, frozen LLM. The modifications lead to a model that can answer graph-related questions - from simple graphs to proteins to hypergraphs - effectively and with minimal, if any, adjustments to the architecture.