Try Gemma.cpp to explain the meaning of each move about katago HOT 3 OPEN

If you are interested in a research project, you might have all of the relevant tools necessary already at your disposal. There is a recent paper about using a LLM to interpret internal model parameters. If you can use an already trained LLM and give it access to the model parameters and the reviews (which are a freely available collection of game reviews from the Go Teaching Ladder) then you might be able to train the LLM to comment games and explain moves as a human would.

However, this is a non-trivial task and a non-trivial ask. I would be interested if you make anything out of it, using either Gemma or OPT or Llama2 or whichever.

from katago.

Probably not. General-purpose LLMs right now aren't going to be very good at Go, and will have almost no training data for interpreting the stats of a tree of nodes from MCTS in Go.

from katago.

Probably not. General-purpose LLMs right now aren't going to be very good at Go, and will have almost no training data for interpreting the stats of a tree of nodes from MCTS in Go.

Is there any other possibility then? We know that Go AI has achieved a certain 'god-like' level through extensive self-play. By analogy, could we apply a fine-tuning approach similar to the one used for large language models (LLMs) to Katago? In this case, the objective would be to make Katago understand human game records (even those of specific players) based on its existing model parameters, with the goal of identifying the positions where humans are most prone to making mistakes.

from katago.