Future of Information Retrieval Research in the Age of Generative AI

• tag:aim-pro.eu,2026:oer/8500fe8c5b59
• https://arxiv.org/abs/2412.02043
• arxiv:2412.02043

This engine's id, the source URI, and any persistent identifier the source published.

• Future of Information Retrieval Research in the Age of Generative AI

The title of this resource, not of the record or repository it came from.

• dcmitype:Text
• preprint

A DCMI Type term for the form held, plus the source's own genre label as declared.

• James Allan
• Eunsol Choi
• Daniel P. Lopresti
• Hamed Zamani

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• In the fast-evolving field of information retrieval (IR), the integration of generative AI technologies such as large language models (LLMs) is transforming how users search for and interact with information. Recognizing this paradigm shift at the intersection of IR and generative AI (IR-GenAI), a visioning workshop supported by the Computing Community Consortium (CCC) was held in July 2024 to discuss the future of IR in the age of generative AI. This workshop convened 44 experts in information retrieval, natural language processing, human-computer interaction, and artificial intelligence from academia, industry, and government to explore how generative AI can enhance IR and vice versa, and to identify the major challenges and opportunities in this rapidly advancing field. This report contains a summary of discussions as potentially important research topics and contains a list of recommendations for academics, industry practitioners, institutions, evaluation campaigns, and funding agencies.

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• cs.IR (arxiv)
• cs.AI (arxiv)

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• 2024-12-03

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• doi:10.1007/978-3-031-56069-9_57
• doi:10.1145/3626772.3657994
• arXiv:2405.11612
• doi:10.1145/3642979.364300
• doi:10.1145/3477495.3531722
• doi:10.1145/3626772.3657848

Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.

• Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
• Text extracted from pdf to Markdown by pdf; the original is retained unchanged beside it.

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• URI: tag:aim-pro.eu,2026:oer/8500fe8c5b59
• URI: https://arxiv.org/abs/2412.02043
• ARXIV: 2412.02043

This engine's identifier for the resource, the source URI, and any persistent identifier the source published.

• Future of Information Retrieval Research in the Age of Generative AI

• en

The language of the content, as the language gate read it off the converted text. The language the source declared is kept separately.

• In the fast-evolving field of information retrieval (IR), the integration of generative AI technologies such as large language models (LLMs) is transforming how users search for and interact with information. Recognizing this paradigm shift at the intersection of IR and generative AI (IR-GenAI), a visioning workshop supported by the Computing Community Consortium (CCC) was held in July 2024 to discuss the future of IR in the age of generative AI. This workshop convened 44 experts in information retrieval, natural language processing, human-computer interaction, and artificial intelligence from academia, industry, and government to explore how generative AI can enhance IR and vice versa, and to identify the major challenges and opportunities in this rapidly advancing field. This report contains a summary of discussions as potentially important research topics and contains a list of recommendations for academics, industry practitioners, institutions, evaluation campaigns, and funding agencies.

The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.

• cs.IR
• cs.AI

• v1

• James Allan — author
• Eunsol Choi — author
• Daniel P. Lopresti — author
• Hamed Zamani — author

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• https://arxiv.org/abs/2412.02043

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• references: doi:10.1007/978-3-031-56069-9_57
• references: doi:10.1145/3626772.3657994
• references: arXiv:2405.11612
• references: doi:10.1145/3642979.364300
• references: doi:10.1145/3477495.3531722
• references: doi:10.1145/3626772.3657848

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