There are plenty of examples of encoders, like this paper, that are being applied to read through all literature, but using an LLM directly (typically referring to the decoder) is not really necessarily useful or efficient (for typical tasks done in the field). It's far more efficient to use smaller models for these tasks, because, for example, if you have 60 million articles with ~2000 tokens, and your system can process only 30 tokens per second, you're looking at 16 *years* to process the whole dataset. 1*10^1 tokens/second is the order of magnitude that LLMs work in, and maybe 10^2 regime if you have a machine you purchase for around $1 million. So that's years of processing for any typical researcher trying to do this, or months of you have a small amount (single millions) of investment. If it's billions, more possibilities open up as is clear with Deepmind et al.

So clearly you need to have some very good hardware to process all of it.

However, compare that with some of the simpler encoder models that have far fewer params that are targeted for specific tasks. These systems can plow through 10^5 or 10^6 tokens per second. So now that 16 years is a week.

This is why small models that are task specific are so important. They make much more possible in reasonable time frames and reduce CO2 emissions by orders of magnitude. Along the same lines of "why use an LLM to extract everywhere the string 'Starbucks4{:digit:}' appears in text when you can use a regex?". You can get the output in a few seconds on billions of articles with a DB, whereas an LLM would take more than a decade.