How can transfer learning in transformer architecture improve sequence-to-sequence learning, and what ethical considerations should businesses keep in mind when implementing these AI technologies?

Transfer learning lets a transformer that has already learned language patterns from a huge dataset be fine‑tuned on a smaller, task‑specific dataset, so the model can quickly adapt to new sequence‑to‑sequence jobs like translation or summarization with fewer examples. For instance, a model pre‑trained on billions of English sentences can be retrained on a small set of legal documents to translate contracts, saving time and data. This approach speeds learning and improves accuracy because the transformer already knows useful word relationships and attention patterns. Businesses must guard against biased data, ensure transparency in how the model was trained, protect user privacy, and consider the impact of errors on real‑world decisions.