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DAST (Diversifying Arithmetic Sampling with TokenTour), a method that increases the diversity of LLM outputs without any change to the marginal distribution and with negligible generation-time overhead, and significantly improves performance on the downstream task of ProtoQA.
This work proposes AgentRecommender, a method that leverages the investigation capability and internal knowledge of LLM agents to flexibly build user-side recommender systems without additional data, which allows users to easily create recommender systems tailored to their own preferences.
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