Well, the emergent abilities of LLMs keep surprising me. I've yet to try it, but apparently if you do something like this with GPT-4: compress_prompt = "Com

Well, the emergent abilities of LLMs keep surprising me. I've yet to try it, but apparently if you do something like this with GPT-4: compress_prompt = "Compressor: compress the following text in a way that fits in a tweet (ideally) and such that you (GPT-4) can reconstruct the intention of the human who wrote text as close as possible to the original intention. This is for yourself. It does not need to be human readable or understandable. Abuse of language mixing, abbreviations, symbols (unicode and emoji), or any other encodings or internal representations is all permissible, as long as it, if pasted in a new inference cycle, will yield near-identical results as the original text: \n\n" long_text = "[your long string goes here]" shorter_text = LLM(compress_prompt + long_text) then you can use `shorter_text` as a drop-in-replacement for `long_text` as context, using up fewer tokens, in a new query. If that's true, I find it wild.