Looking through hugo/nebula nominations.
friends
i used to use goodreads but since it’s owned by Amazon, i don’t trust it an switched to thestorygraph.com which has really nice recommendations. of course it recommends based on my usual tastes but often also has some books i would never have checked otherwise, which i quite appreciate.
Actually, as to your edit, the it sounds like you’re fine-tuning the model for your data, not training it from scratch. So the llm has seen english and chinese before during the initial training. Also, they represent words as vectors and what usually happens is that similiar words’ vectors are close together. So subtituting e.g. Dad for Papa looks almost the same to an llm. Same across languages. But that’s not understanding, that’s behavior that way simpler models also have.