Knowledge Graphs and Large Language Models (LLMs) Together [part 2].
LLMs are increasingly present in our daily lives: they answer questions, generate texts, summarize information and much more. But despite their amazing ability to deal with natural language, these models have limitations: they can “make up” facts, confuse concepts or lack access to up-to-date or reliable knowledge. And this is where knowledge graphs come in. These structures organize information in a precise and relational way, allowing LLMs to draw on well-organized and verifiable data. We explore how knowledge graphs can become a key ally in improving the accuracy, transparency and reliability of language models, helping them “really know” what they are talking about.
Knowledge Graphs and Large Language Models (LLMs) Together [part 1]
Nowadays we hear all the time about artificial intelligence and, in particular, about large language models, known as Large Language Models or LLMs. These tools, the most famous of which is ChatGPT, are capable of understanding and generating text in a surprisingly natural way and are finding applications in so many areas, from automatic writing to scientific research. One of the most promising uses in recent years is the generation and curation of knowledge graphs, a graph representation of information of interest, where concepts and relationships between them are linked in a structured way with semantic meaning.
