KID-22/LLM-IR-Bias-Fairness-Survey
This is the repo for the survey of Bias and Fairness in IR with LLMs.
Provides a systematically organized paper collection categorizing bias and fairness issues across three stages of LLM-IR integration (data collection, model development, result evaluation), unified under a distribution mismatch framework. Papers are tagged with specific bias types (source bias, factuality bias) and mitigation strategies (data sampling, distribution reconstruction, regularization, prompting). Designed to support the KDD 2024 and WSDM 2025 tutorial series on emerging fairness challenges in LLM-powered retrieval systems.
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Sep 04, 2025
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