Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness
This addresses fairness issues in AI recommendations for diverse demographic and cultural groups, though it is incremental as it builds on existing bias mitigation methods.
The study tackled bias in large language model-based recommendation systems, revealing its pervasive impact across models like GPT, LLaMA, and Gemini, and showed that simple interventions like prompt engineering can significantly reduce it, with numerical experiments validating these strategies.
Large Language Model (LLM)-based recommendation systems excel in delivering comprehensive suggestions by deeply analyzing content and user behavior. However, they often inherit biases from skewed training data, favoring mainstream content while underrepresenting diverse or non-traditional options. This study explores the interplay between bias and LLM-based recommendation systems, focusing on music, song, and book recommendations across diverse demographic and cultural groups. This paper analyzes bias in LLM-based recommendation systems across multiple models (GPT, LLaMA, and Gemini), revealing its deep and pervasive impact on outcomes. Intersecting identities and contextual factors, like socioeconomic status, further amplify biases, complicating fair recommendations across diverse groups. Our findings reveal that bias in these systems is deeply ingrained, yet even simple interventions like prompt engineering can significantly reduce it. We further propose a retrieval-augmented generation strategy to mitigate bias more effectively. Numerical experiments validate these strategies, demonstrating both the pervasive nature of bias and the impact of the proposed solutions.