Yann LeCun is obviously a legend but I found this tweet to be quite misinformed.

The whole point of \"emergent abilities\" such as few-shot prompting and chain-of-thought prompting, is that we clearly *did not* explicitly train or fine-tune them into the model. These abilities require scaling up to a lot of parameters, which is the reason we call them \"emergent\"---they are quantitive behavior shifts that come from qualitative changes. At least this is how researchers use the term.

All we did was train a language model with a lot of parameters on a big dataset. These abilities were not intentionally added into the model during training---rather, people found them via evaluations after the model was trained. A great pointer to this is Jacob Steinhardt\'s post: https://t.co/Y47ZuzkL00

What\'s also significant is that we often aren\'t able to predict these emergent phenomena ahead of time, and this has safety implications since risks can also be emergent.

Also, I don\'t know why finetuning is mentioned, because these abilities are clearly in the base model, which is should be apparent from papers before RLHF became popular... (e.g., PaLM-1, GPT-3 paper)

But even if you insist to take a finetuning as an example, ChatGPT RLHF was originally done in English, and it extended to many other languages. This multilingual ability was not \"finetuned\" into the model, which is a pretty good example of zero-shot generalization.

Please correct me if I\'m wrong, but the technical use of the term definitely still holds.

Mustafa\'s tweet is also a bit misinformed, in my circles people don\'t use \"emergence\" to mean autonomy or agency. Again, what AI researchers usually mean is that it is a qualitative change that arises from quantitative changes.

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