What they are and how they can accelerate LLM training
IE people and obesity, people and mortality. Can you find correlations between the two?
The next layer up is to prove the trust in the data and your insights.
Also you are a decade late, google already bought freebase and made schema.org
Some time back I had a peek at AstraZeneca's GitHub [0] and got me curious. I know in genomics they try to use custom hardware to accelerate the process using FPGAs and others [1].
Curious if anyone can shed light on knowledge graph use at scale is being accelerated.
[0] AstraZeneca; Awesome Drug Discovery Knowledge Graphs https://github.com/AstraZeneca/awesome-drug-discovery-knowle...
[1] Gene sequencing accelerates with custom hardware https://www.mewburn.com/news-insights/gene-sequencing-accele...
There might be more publications on mining literature and building such graphs but I'm not following it much since deep learning took over.
Generative AI models such as large language models typically leverage huge amounts of unstructured data during their initial training and fine-tuning. Knowledge graphs can be leveraged to identify the structure of the underlying data which can then improve the amount of signal which is extracted from the data during the training.
Which is too vague to be of interest, generally uninformative, and furthermore to me reeks of LLM-generated babble instead of something a human wrote.
While there are a few papers on using KG/Ontologies to enhance training, this is really far from the mainstream, and i would be surprised if it would be used anywhere (outside of a research paper)