117 citations · 254 across the 9 of their papers we have counts for
10 papers
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…
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…
LLM-Assisted Relevance Assessments: When Should We Ask LLMs for Help?
Rikiya Takehi, Ellen M. Voorhees, Tetsuya Sakai +1
Test collections are information-retrieval tools that allow researchers to quickly and easily evaluate ranking algorithms. While test collections have become an integral part of IR…
Human Preferences as Dueling Bandits
Xinyi Yan, Chengxi Luo, Charles L. A. Clarke +3
The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items…
Can Old TREC Collections Reliably Evaluate Modern Neural Retrieval Models?
Ellen M. Voorhees, Ian Soboroff, Jimmy Lin
Neural retrieval models are generally regarded as fundamentally different from the retrieval techniques used in the late 1990's when the TREC ad hoc test collections were construct…
Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID
Kirk Roberts, Tasmeer Alam, Steven Bedrick +6
We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COV…