AINeutralarXiv – CS AI · Apr 67/10
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Jump Start or False Start? A Theoretical and Empirical Evaluation of LLM-initialized Bandits
Research examines how Large Language Models can be used to initialize contextual bandits for recommendation systems, finding that LLM-generated preferences remain effective up to 30% data corruption but can harm performance beyond 50% corruption. The study provides theoretical analysis showing when LLM warm-starts outperform cold-start approaches, with implications for AI-driven recommendation systems.