LexiSearch is upgrading its document ranking stack for enterprise knowledge search. The team wants an NLP engineer to explain the Transformer architecture clearly and justify why it replaced older sequence models such as RNNs and LSTMs in modern language systems.
You are given a corpus of 2.5 million English documents and 180,000 query-document relevance labels. Query length ranges from 2-20 tokens, while documents range from 30-512 tokens after truncation. Relevance labels are imbalanced: 68% not relevant, 22% partially relevant, and 10% highly relevant. Text includes product names, abbreviations, punctuation-heavy logs, and repeated boilerplate.
A strong answer should accurately describe self-attention, positional encoding, multi-head attention, feed-forward layers, residual connections, and encoder-decoder structure. It should also connect the architecture to practical benefits: parallelization, long-range dependency modeling, and transfer learning performance.