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What are vector embeddings, and why are they important in LLMs?
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Answer Posted / Raju Sharma
Vector embeddings are a way of representing words, phrases, and other textual entities as high-dimensional vectors in a continuous space. In Language Models (LLMs), vector embeddings play an essential role by allowing the model to understand the semantic relationships between words and phrases. This is crucial for tasks such as text generation, translation, summarization, and sentiment analysis.
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