The thing is, we typically have three parts of a compression scheme: the data, the model (dictionary, Huffman tree, etc), and the program. The model is typically learned from the data at compression time.
The program itself is general, however, and as far as I know, doesn't count against the total. Ie, the size of the gzip binary isn't part of the total for the purposes of the contest.
So if an LLM is genuinely useful without fine tuning on the target data, you should make it part of the program.
The data-specific model could be a self-prompt produced from reading and summarizing the data, which would help with the initial context-free predictions...
{Edit} Ha, looking at the competition, it does indeed include the full size of the gzip binary in the metric. Weird.
> {Edit} Ha, looking at the competition, it does indeed include the full size of the gzip binary in the metric. Weird.
The competition is specific to one corpus of test data; it doesn't include testing on other data. If the size of the program weren't included, you could hard-code all of the test data into it and claim victory with an infinite compression ratio.
Fun fact: the idea of using an AI language model as compression has existed since at least 1988. It was mentioned in passing by Vernor Vinge in his short story The Blabber.
The program itself is general, however, and as far as I know, doesn't count against the total. Ie, the size of the gzip binary isn't part of the total for the purposes of the contest.
So if an LLM is genuinely useful without fine tuning on the target data, you should make it part of the program.
The data-specific model could be a self-prompt produced from reading and summarizing the data, which would help with the initial context-free predictions...
{Edit} Ha, looking at the competition, it does indeed include the full size of the gzip binary in the metric. Weird.