[R] Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models

PDF: [https://arxiv.org/abs/2404.11457](https://arxiv.org/abs/2404.11457)

GitHub: [https://github.com/KID-22/LLM-IR-Bias-Fairness-Survey](https://github.com/KID-22/LLM-IR-Bias-Fairness-Survey)

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Abstract: With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift. This evolution, while heralding new opportunities, introduces emerging challenges, particularly in terms of biases and unfairness, which may threaten the information ecosystem. In this paper, we present a comprehensive survey of existing works on emerging and pressing bias and unfairness issues in IR systems when the integration of …

abstract advancement biases challenges ecosystem evolution information language language models large language large language models llms machinelearning opportunities paper paradigm recommender systems retrieval search shift survey systems terms the information