On average, the feature-aware models assigned a log likelihood of −20.98 to the words in the test set, and the feature-naive models assigned an average log likelihood of −20.07.
the feature-naive models assigned over twice the probability mass to the test set compared to the feature-aware models, in terms of raw (non-log) probability.