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Mean-initializing new tokens in LMs creates a degenerate embedding space that cripples fine-tuning, but a simple "grounding" step can unlock significant performance gains in generative recommendation.
Conformal factuality for RAG breaks down when faced with distribution shifts or distractors, forcing a trade-off between factuality and informativeness.
