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 into the architecture of such systems. This integration raises concerns that it might exacerbate popularity bias%2C given that the LLM's training data is likely dominated by popular items. However%2C it simultaneously presents a novel opportunity to address the bias via prompt tuning. Our study explores this dichotomy%2C examining whether LLMs contribute to or can alleviate popularity bias in recommender systems. We introduce a principled way to measure popularity bias by discussing existing metrics and proposing a novel metric that fulfills a series of desiderata. Based on our new metric%2C we compare a simple LLM-based recommender to traditional recommender systems on a movie recommendation task. We find that the LLM recommender exhibits less popularity bias%2C even without any explicit mitigation.?quality=80&w=800)
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