The research on GPT-style language models is fascinating and the possibilities are seemingly endless. We’ve seen that these models can be used to play games like Othello and can even understand different languages in a single phrase. But how exactly do such complex functions work?
The answer to this is: “we don’t really know as its a very complex function automatically discovered by means of slow gradient descent, and we’re still finding out”. In other words, this technology is still relatively new and there’s a lot of research being done to better understand it.
We have seen some amazing results from GPT-style language models so far, such as using 64 probes to look at an OthelloGTP’s internals and being able to reduce error rates from 26.2% on a randomly-initialized Othello-GPT to only 1.7%. This suggests that there exists a world model in the internal representation of a trained Othello-GPT, which means it’s not simply relying on statistical word jumbles but rather has some sort of internal model based on correlations between its internal state and what we know the board should look like at each step in the game.
This technology also has implications beyond playing games or understanding languages – it could be used for sentiment analysis or predicting stock market trends etc., all with surprising accuracy! By understanding how these complex functions work better, we can use them for even more applications than what we thought possible before!
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Author Eliza Ng