The model outputs words, not tokens, so that is not a great example. Virtually all modern transformer models use pieces, which may be words, but also subwords.

The model outputs words, not tokens, so that is not a great example. Virtually all modern transformer models use pieces, which may be words, but also subwords. Theoretically, they could be longer units, but in most cases some characters (like whitespace) are used as piece boundaries when training the piece vocabulary. If they didn’t use pieces, they’d work terribly on languages where e.g. compounds are a single word. In most realistic piece vocabs, ‘elephant’ will be a single piece, since it’s a fairly frequent word. But it’s totally possible in a small vocab that it would be split like the parent said and conversely, it would generate elephant by first predicting one piece. Some piecing methods, like BBPE have bytes as the smallest unit, so theoretically an unknown token could be split up (and generated) as pieces consisting of bytes.