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Background The paper discusses the swift integration and application of large language models (LLMs) in information retrieval (IR) systems, where this progress not only introduces new opportunities but also presents new challenges of bias and unfairness, potentially posing threats to the information ecosystem.
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Existing Work The existing literature lacks a unified definition of bias and unfairness within LLMs and IR systems, which impedes the development of effective systematic strategies to address these issues.
- A novel unified perspective
- Challenge 1: Distribution Mismatch Issue The challenge lies in how to treat the existing biases and unfairness issues as distribution mismatch problems accurately. It is challenging to identify and understand the fact that the predictive information lacks objectivity and truthfulness, and doesn't match with the objective target distribution. The paper resolves this challenge by providing a unified perspective to understand bias and unfairness as distribution mismatch problems and outlines various strategies for mitigation.
While the paper did not provide experimental results or specific comparisons with related work, it offers a novel perspective for analyzing and understanding bias and unfairness issues in LLMs and IR systems, with an emphasis on the origins of these problems and strategies for their mitigation. The survey reviews a substantial body of relevant literature to build this perspective, from which it derives a taxonomy of mitigation strategies. Although actual deployment and evaluation outcomes are not provided, this unified perspective itself is a valuable contribution to enhancing the understanding and resolution of these issues.
The survey paper offers a new perspective for understanding bias and unfairness in LLMs and IR systems as distribution mismatch problems and categorizes various mitigation strategies.