Ask it what material absorbs “infrared light” efficiently.
To me, that’s useful intelligence. I can already search text for verbatim matches, I want the AI to understand that “thermal radiations” and “infrared light” are the same thing.
> Answer the following question using verbatim quotes from the text above: "What material absorbs infrared light efficiently?"
> "Graphene is a promising material that could change the world, with unlimited potential for wide industrial applications in various fields... It is the thinnest known material with zero bandgaps and is incredibly strong, almost 200 times stronger than steel. Moreover, graphene is a good conductor of heat and electricity with very interesting light absorption properties."
Interestingly, the first sentence of the response actually occures directly after the latter part of the response in the original text.
Edit: asking it "What absorbs infrared light and converts it into electrical signals?" yields "Graphene sheets are highly transparent presenting high optical transparency, which absorbs thermal radiations with high efficacy and converts it into electrical signals efficiently." verbatim.
Fair point, but I also think something that's /really/ clear is that LLMs don't understand (and probably cannot). It's doing highly contextual text retrieval based on natural language processing for the query, it's not understanding what the paper means and producing insights.
To me, that’s useful intelligence. I can already search text for verbatim matches, I want the AI to understand that “thermal radiations” and “infrared light” are the same thing.