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Measuring and Mitigating Identity-Cue Preference Drift in LLM-based Recommender Systems
In large language model-based recommender systems, identity cues embedded in prompts can steer recommendations toward group-level patterns even when the underlying behavioral evidence remains unchanged. We introduce PromptShift, an interpretable, training-free framework for quantifying and mitigatin
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