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Forget contrastive learning: LLM2Vec-Gen trains special tokens to represent the *response* an LLM would give, yielding SOTA embeddings with better safety and reasoning.
Post-training LLMs is more about finding the right "key" (a few kilobytes of parameters) to unlock pre-existing knowledge than learning new information.
