Part 6/14:
From Words to Sentences: Capturing Context
A crucial challenge is translating entire sentences or documents into meaningful vectors. Early methods treated words independently, but LLMs have advanced this by creating sentence embeddings that account for context, syntax, and semantics holistically.
For example, the phrase "I am good" and "I am no good" might share token-level similarities but differ significantly in intent. Embeddings trained on extensive data understand these nuances, placing similar meanings closer together even if the wording differs.