Faithful Reasoning Using Large Language Models

Is it only me or does the bear training data in Figure 13 not make sense? “Q: Is the bear loud? A: The bear is soft.”

And what are we to make of the reasoning step that “All round things are loud. The bear is sound. Therefore the bear is soft.” The bear is sound? What does that even mean?

Is that a typo that should read, “the bear is round”? If so, why isn’t the conclusion that the bear is loud rather than soft? From the context we do not know that all round things are soft, only that all soft things are round.