Conversing with Data: How AI Makes Sense of Graphs

Ever thought about how AI can interpret complex connections like those in social networks or even our own bodies? Big language models, or LLMs, are throwing open doors in this sphere. Unlike needing tailor-made training, LLMs can tackle various tasks right off the bat. But how do they interact with graph data? Let's take LLM-GQP for instance. It melds graph techniques with model prompts, like asking questions of a digital map to find specific data. Then there's LLM-GIL, where the model learns from graphs, reasons with them, and turns theinformation into a format it can understand. Prompts play a vital role here—they guide the LLMs on what to do. But it's not all smooth sailing. The article delves into the pros and cons of these models, their tricky aspects, and what the future might hold in terms of advancements and research.