
The authors introduce CFaiRLLM, a framework to evaluate consumer fairness in RecLLMs, focusing on biases introduced by sensitive attributes. They emphasize the importance of aligning recommendations with user preferences to ensure fairness.


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cfairllm-consumer-fairness-evaluation-in-large-language-model-recommender-system


CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System


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Large Language Models (LLMs) in recommender systems raise concerns about fairness, prompting the need for a comprehensive evaluation framework like CFaiRLLM to address biases and ensure equitable recommendations.
