We are indirectly applying derogatory language to the training set, which is our own culture. Strange position to despise our own past knowledge, took us a long

We are indirectly applying derogatory language to the training set, which is our own culture. Strange position to despise our own past knowledge, took us a long time to compile it. Even if someone thinks LLMs are just stochastic parrots, they parrot our own thoughts. But if we accept LLMs as more than parrots, like an improv jazz musician adapting to the audience and always creating something new recombining the old in novel ways, then it is even less justified to despise LLM outputs. The whole parrot idea is half baked. Yes, in closed book mode, and without a seed prompt that ads anything new to the existing knowledge of the model, it will parrot (adapt) from its training data. But if you add novel data in the prompt, then we push it outside its training distribution, and it learns on the fly. And usually all interactions bring some new elements to the model, new knowledge or skills, or new context. The more elaborate the prompts, the longer the session of messages, the less parroting takes place. The O3 model performance on ARC-AGI is another argument against the reductionist idea of parroting.