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cs.IR2025

Emancipatory Information Retrieval

Bhaskar Mitra

Our world today is facing a confluence of several mutually reinforcing crises each of which intersects with concerns of social justice and emancipation. This paper is a provocation…

cs.IR2025

Towards Understanding Bias in Synthetic Data for Evaluation

Hossein A. Rahmani, Varsha Ramineni, Emine Yilmaz +2

Test collections are crucial for evaluating Information Retrieval (IR) systems. Creating a diverse set of user queries for these collections can be challenging, and obtaining relev…

cs.IR2025

Overview of the TREC 2022 deep learning track

Nick Craswell, Bhaskar Mitra, Emine Yilmaz +4

This is the fourth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels…

cs.IR2025

Overview of the TREC 2023 deep learning track

Nick Craswell, Bhaskar Mitra, Emine Yilmaz +5

This is the fifth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human-annotated training labels a…

cs.IR2025

Overview of the TREC 2021 deep learning track

Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2

This is the third year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels a…

cs.IR2025

Tip of the Tongue Query Elicitation for Simulated Evaluation

Yifan He, To Eun Kim, Fernando Diaz +2

Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effective…