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%20has%20led%20to%20an%20influx%20of%20AI-generated%20content%20(AIGC)%20on%20the%20internet%2C%20transforming%20the%20corpus%20of%20Information%20Retrieval%20(IR)%20systems%20from%20solely%20human-written%20to%20a%20coexistence%20with%20LLM-generated%20content.%20The%20impact%20of%20this%20surge%20in%20AIGC%20on%20IR%20systems%20remains%20an%20open%20question%2C%20with%20the%20primary%20challenge%20being%20the%20lack%20of%20a%20dedicated%20benchmark%20for%20researchers.%20In%20this%20paper%2C%20we%20introduce%20Cocktail%2C%20a%20comprehensive%20benchmark%20tailored%20for%20evaluating%20IR%20models%20in%20this%20mixed-sourced%20data%20landscape%20of%20the%20LLM%20era.%20Cocktail%20consists%20of%2016%20diverse%20datasets%20with%20mixed%20human-written%20and%20LLM-generated%20corpora%20across%20various%20text%20retrieval%20tasks%20and%20domains.%20Additionally%2C%20to%20avoid%20the%20potential%20bias%20from%20previously%20included%20dataset%20information%20in%20LLMs%2C%20we%20also%20introduce%20an%20up-to-date%20dataset%2C%20named%20NQ-UTD%2C%20with%20queries%20derived%20from%20recent%20events.%20Through%20conducting%20over%201%2C000%20experiments%20to%20assess%20state-of-the-art%20retrieval%20models%20against%20the%20benchmarked%20datasets%20in%20Cocktail%2C%20we%20uncover%20a%20clear%20trade-off%20between%20ranking%20performance%20and%20source%20bias%20in%20neural%20retrieval%20models%2C%20highlighting%20the%20necessity%20for%20a%20balanced%20approach%20in%20designing%20future%20IR%20systems.%20We%20hope%20Cocktail%20can%20serve%20as%20a%20foundational%20resource%20for%20IR%20research%20in%20the%20LLM%20era%2C%20with%20all%20data%20and%20code%20publicly%20available%20at%20url%7Bhttps:%2F%2Fgithub.com%2FKID-22%2FCocktail%7D.)
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