[VSAONLINE] Starts in TWO hours!
Evgeny Osipov
Dear all,
This is a reminder about today’s VSAONLINE webinar as per description below. Please mind the seasonal time shift. Sere you in about TWO hours .
Best
Evgeny
Welcome to the next talk of Season 12 on VSAONLINE. Sotirios Panagiotis (Takis) Chytas
from University of Wisconsin-Madison , USA will give a talk
”FoGE: Fock Space inspired encoding for graph prompting”
Date: March 30, 2026
Time: 20:00 GMT
Zoom: https://ltu-se.zoom.us/j/65564790287
Abstract: Recent results show that modern Large Language Models (LLM) are indeed capable of understanding and answering questions about structured data such as graphs. This new paradigm can lead to solutions that require less supervision while, at the same time, providing a model that can generalize and answer questions beyond the training labels. 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. Existing solutions to graph-basedprompts 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 mathematical physics, is remarkably versatile in this problem setting. The simple construction, inherited directly from the theory 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. Our work significantly simplifies existing solutions and generalizes well to multiple different graph-based structures effortlessly.