A multi-objective prompt optimization framework for LLM user simulators in conversational recommender systems improves behavioral alignment with human patterns over baselines.
A survey on large language models for recommendation
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
GenPOI is a generative POI retrieval system that unifies heterogeneous contexts via LLMs, uses geo-semantic tokenization, and applies proximity constraints to achieve superior performance on large-scale map search data.
Dual-Stream Calibration uses entropy minimization and iterative meta-learning at test time to internalize clinical evidence and outperform standard in-context learning baselines on medical tasks.
citing papers explorer
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Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework
A multi-objective prompt optimization framework for LLM user simulators in conversational recommender systems improves behavioral alignment with human patterns over baselines.
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Revisiting General Map Search via Generative Point-of-Interest Retrieval
GenPOI is a generative POI retrieval system that unifies heterogeneous contexts via LLMs, uses geo-semantic tokenization, and applies proximity constraints to achieve superior performance on large-scale map search data.
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From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning
Dual-Stream Calibration uses entropy minimization and iterative meta-learning at test time to internalize clinical evidence and outperform standard in-context learning baselines on medical tasks.