I think there are two different layers that get frequently mixed.
(1) LLMs as models - just the weights and an inference engine. These are just tools like hammers. There is a wide variety of models, starting from transparent and useless IBM Granite models, to open-weights Llama/Qwen to proprietary.
(2) AI products that are built on top of LLMs (agents, RAG, search, reasoning etc). This is how people decide to use LLMs.
How these products display results - with or without citations, with or without attribution - is determined by the product design.
It takes more effort to design a system that properly attributes all bits of information to the sources, but it is doable. As long as product teams are willing to invest that effort.
(1) LLMs as models - just the weights and an inference engine. These are just tools like hammers. There is a wide variety of models, starting from transparent and useless IBM Granite models, to open-weights Llama/Qwen to proprietary.
(2) AI products that are built on top of LLMs (agents, RAG, search, reasoning etc). This is how people decide to use LLMs.
How these products display results - with or without citations, with or without attribution - is determined by the product design.
It takes more effort to design a system that properly attributes all bits of information to the sources, but it is doable. As long as product teams are willing to invest that effort.