Consider two different methods for creating contextualized numerical representations of words in a sentence. Method 1 generates a representation for each word based only on the words that precede it. Method 2 generates a representation for each word based on all other words in the sentence, both preceding and succeeding it. Which statement accurately compares these two methods to the processes found in large-scale language models?

Directionality in Contextual Representations

The primary functional difference between the prefilling phase in an autoregressive model and the encoding process in a model like BERT is the specific mathematical operations used to create token representations; their approach to incorporating contextual information from the input sequence is fundamentally identical.