alt Hacker NewsThat is what I am looking for. a) LLMs are trained using compressed text tokens and b) use compressed prompts. Don't know how..but that is what I was hoping for.
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rattyJ2 • today at 7:12 AM
Tokens are already compressed. That's what tokenisation is.
The whole point of embeddings and tokens are that they are a compressed version of text, a lower dimensionality. now, how low depends on performance, lower amount of vectors=more lossy (usually). https://huggingface.co/spaces/mteb/leaderboard
You can train your own with very very compressed, i mean you could even go down to each token=just 2 float numbers. It will train, but it will be terrible, because it can essentially only capture distance.
Prompting a good LLM to summarize the context is probably funnily enough the best way of actually "compressing" context