大语言模型推荐系统中的不确定性与公平性意识
Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems
AI总结：
本文研究了大语言模型推荐系统中不确定性与公平性的影响，提出了一种新的评估方法并揭示了个性化与公平性之间的权衡。
摘要：
大语言模型（LLMs）通过利用广泛的上下文知识实现了强大的零样本推荐，但预测不确定性与嵌入的偏见威胁着可靠性和公平性。本文研究了不确定性与公平性评估如何影响LLM生成推荐的准确性、一致性和可信度。我们引入了一个经过精心挑选的指标基准和一个标注了八个人口属性（31个分类值）的双领域数据集（电影和音乐）。通过深入的案例研究，我们量化了预测不确定性（通过熵）并展示了Google DeepMind的Gemini 1.5 Flash在某些敏感属性上存在系统性不公平；测量的相似性基于差距为SNSR 0.1363和SNSV 0.0507。这些差异在提示扰动如拼写错误和多语言输入下仍然存在。我们进一步将个性感知的公平性整合到RecLLM评估流程中，以揭示与个性相关的偏见模式并暴露个性化与群体公平之间的权衡。我们提出了一种新的不确定性意识评估方法用于RecLLMs，展示了深度不确定性案例研究的实证见解，并引入了一个基于个性轮廓的公平性基准，以推动LLM推荐的可解释性和公平性。这些贡献为更安全、更可解释的RecLLMs奠定了基础，并激励了未来关于多模型基准和适应性校准的研究。
英文摘要：
Large language models (LLMs) enable powerful zero-shot recommendations by leveraging broad contextual knowledge, yet predictive uncertainty and embedded biases threaten reliability and fairness. This paper studies how uncertainty and fairness evaluations affect the accuracy, consistency, and trustworthiness of LLM-generated recommendations. We introduce a benchmark of curated metrics and a dataset annotated for eight demographic attributes (31 categorical values) across two domains: movies and music. Through in-depth case studies, we quantify predictive uncertainty (via entropy) and demonstrate that Google DeepMind's Gemini 1.5 Flash exhibits systematic unfairness for certain sensitive attributes; measured similarity-based gaps are SNSR at 0.1363 and SNSV at 0.0507. These disparities persist under prompt perturbations such as typographical errors and multilingual inputs. We further integrate personality-aware fairness into the RecLLM evaluation pipeline to reveal personality-linked bias patterns and expose trade-offs between personalization and group fairness. We propose a novel uncertainty-aware evaluation methodology for RecLLMs, present empirical insights from deep uncertainty case studies, and introduce a personality profile-informed fairness benchmark that advances explainability and equity in LLM recommendations. Together, these contributions establish a foundation for safer, more interpretable RecLLMs and motivate future work on multi-model benchmarks and adaptive calibration for trustworthy deployment.
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