Why combine GraphML with LLMs?
As we have learned throughout this book, GraphML excels at representing and analyzing structured data such as knowledge graphs, social networks, chemical structures, and so on. It is extremely useful for situations where exploiting relationships between entities is crucial for achieving good performances. However, LLMs are particularly good at interpreting unstructured text, offering generative skills, reasoning, and profound contextual awareness. When it comes to language-based activities such as content creation, question answering, and summarization, they excel.
Despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges is the problem of hallucination, where an LLM generates factually incorrect or misleading information that appears plausible. This is particularly problematic in domains requiring high factual accuracy, such as healthcare, finance, and legal applications. To mitigate hallucinations...
