[treyd]

If I had to guess, single characters are able to be encoded as tokens, but there's more "bandwidth" in the model being dedicated to handling them and there's less semantic meaning encoded in them "natively" compared to tokens for concrete words. If it decides to, it can recreate unknown sequences by copying over the tokens for the single letters or create them if it makes sense.

[heisenburgzero]

I think some earlier NLP applications have something called "Unknown token", which they will replace any unseen word. But for recent implementations, I don't think they are being used anymore.

It still baffles me why such stochastic parrot / next token predictor, will recognize these "Unseen combinations of tokens" and reuse them in response.

[stormfather]

This helped me understand but not well enough to explain it yet: https://transformer-circuits.pub/2022/in-context-learning-an...

[heisenburgzero]

Thanks. "In-context learning" was the phrase I was looking for.

Also, I found these 2 links pretty good too. 1. http://ai.stanford.edu/blog/understanding-incontext/ 2. http://ai.stanford.edu/blog/in-context-learning/

I'm still not completely convinced. Probably need to dwell on the topic longer.

[stevenhuang]

Everything falls into place once you understand that LLMs are indeed learning hierarchical concepts inherent in the structured data it has been trained on. These concepts exist in a high dimensional latent space. Within this space is the concept of nonsense/gibberish/placeholder, which your sequence of unseen tokens map to. Then it combines this with the concept of SQL tables, resulting in hopefully the intended answer.