Talk like a graph: Encoding graphs for large language models (blog.research.google)

Posted by Bahare Fatemi and Bryan Perozzi, Research Scientists, Google Research Imagine all the things around you — your friends, tools in your kitchen, or even the parts of your bike. They are all connected in different ways. In computer science, the term graph is used to describe connections between objects. Graphs consist of nodes (the objects themselves) and edges (connections between two nodes, indicating a relationship between them). Graphs are everywhere now. The internet itself is a giant graph of websites linked together. Even the knowledge search engines use is organized in a graph-like way. Furthermore, consider the remarkable advancements in artificial intelligence — such as chatbots that can write stories in seconds, and even software that can interpret medical reports. This exciting progress is largely thanks to large language models (LLMs). New LLM technology is constantly being developed for different uses. Since graphs are everywhere and LLM technology is on the rise, in “ Talk like a Graph: Encoding Graphs for Large Language Models ”, presented at ICLR 2024 , we present a way to teach powerful LLMs how to better reason with graph information. Graphs are a useful way to organize information, but LLMs are mostly trained on regular text. The objective is to test different techniques to see what works best and gain practical insights. Translating graphs into text that LLMs can understand is a remarkably complex task. The difficulty stems from the inherent complexity of graph structures with multiple nodes and the intricate web of edges that connect them. Our work studies how to take a graph and translate it into a format that an LLM can understand. We also design a benchmark called GraphQA to study different approaches on different graph reasoning problems and show how to phrase a graph-related problem in a way that enables the LLM to solve the graph problem. We show that LLM performance on graph reasoning tasks varies on three fundamental levels: 1) the graph encoding method, 2) the nature of the graph task itself, and 3) interestingly, the very structure of the graph considered. These findings give us clues on how to best represent graphs for LLMs. Picking the right method can make the LLM up to 60% better at graph tasks! Pictured, the process of encoding a graph as text using two different approaches and feeding the text and a question about the graph to the LLM. Graphs as text To be able to systematically find out what is the best way to translate a graph to text, we first design a benchmark called GraphQA . Think of GraphQA as an exam designed to evaluate powerful LLMs on graph-specific problems. We want to see how well LLMs can understand and solve problems that involve graphs in different setups. To create a comprehensive and realistic exam for LLMs, we don’t just use one type of graph, we use a mix of graphs ensuring breadth in the number of connections. This is mainly because different graph types make solving such problems