A team is developing two different text generation models, Model A and Model B. Both are given the same input phrase, "The sky is".

• Model A calculates the probability for the next word (e.g., "blue") based on the full input phrase "The sky is".
• Model B calculates the probability for the next word (e.g., "blue") based on the full input phrase "The sky is" AND any words it has already generated in the current output sequence.

One of these models represents a fundamentally flawed approach for generating coherent, multi-word sequences. Identify which model is flawed and explain why its method for determining the probability of the next word is problematic for generating longer, sensible sentences.