FLMs can be trained easily by estimating conditional probabilities from feature counts over a corpus, and they offer efficient decoding techniques for real-time generation.
fLMs do not scale well to large feature sets (i.e., contexts), as each additional feature increases the amount of data required to accurately estimate the fLM's conditional probability distribution.