ChatGPT does not utilize a knowledge graph to understand or generate common sense, then I wonder how knowledge graphs can be utilized in the future. Will they be replaced by LLMs?
A couple of days ago, Jordi Torras from Inbenta posted that chatGPT fails at classifying a particular integer as prime, while their chatbot nails it. But the goal of a chatbot is no way factoring integers, is it?
Some weeks ago, Stephen Wolfram suggested some combination of chatGPT and their WolframAlpha, a curated engine for computational intelligence.
A wealth of domains could benefit from integrating preexisting knowledge into the conversational skill of transformers.
As a simple example, take "explain how 30 is 2x3x5", where the verified information plugged as a prompt may be obtained from a curated system and the natural language exposition could be finally written by a conversational system.
I don't foresee knowledge absorbed by LLM, but some form of combination between both techiques. Consider the times tables, the chemical elements, or lots of well known and established knowledge pieces. Is there any advantage in texting all that structured information to afterwards gradient descent train on it? Not to mention algorithms, from Viterbi to Quick Sort to the Fast Fourier Transform. Those look like specialized intelligence modules to be interfaced by Large Language Models, rather than (re)learned from scratch.
It is correct that curation and annotation are crucial to knowledge graph. At the same time, such annotation has been accumulated intensely in few areas like medical and manufacturing (some publicly, and some internally within the organization) - partly accelerated by the need of data interoperability and standardization within the industry.
So while it may not be very ready yet for generic use cases, some form of knowledge graphs/ontologies are already utilized for a long time in domains mentioned earlier.
Besides that, the current active research on knowledge graph generation/inference will potentially increase the breath and depth of the graph in a more scalable way.