Oh, since the topic of semantics compression via LLMs came up, here is some interesting research result that I had found earlier this year that I posted here and failed to explain properly, with a benchmark as well for you to try on your own if you want.

https://github.com/yuechen-li-dev/GenerativeCompressionProto...

Essentially, copypaste the codeblock in the Markdown into any LLM chat, and it will return with the benchmark results. Very easy benchmark to run.

Essentially, semantic compression refers to reducing the size of a set of data while retaining its full semantic meaning. The useful application of that is of course, with prompt compression to save context. I know a lot of people essentially sends their prompt to another LLM to compress into JSON first before they send it out, and this came out of an experiment to see the best method to accomplish that task, and the idea is that the compressed and uncompressed prompts will return the same result if sent to another LLM.

What that block of Chinese text is essentially a kind of "meta-prompt" that causes the LLM to reflect on itself as well as the method of how to compress information into the highest possible density form, and the reason it is in Chinese is because it is the language with the highest semantic density that I know of. You can ask an LLM to explain what the text in the block means to have an explanation of what everything means and why it works, but overall it tends to greatly increase the efficiency of semantic compression task of turning prose to JSON across the board on pretty much every LLM that I've tested it on.

That's basically the explanation of it, I thought it was a crazy discovery when I found it a couple of months ago, but now I just think it is pretty neat.