70 citations · 220 across the 14 of their papers we have counts for
18 papers
Best-Case Retrieval Evaluation: Improving the Sensitivity of Reciprocal Rank with Lexicographic Precision
Fernando Diaz
Across a variety of ranking tasks, researchers use reciprocal rank to measure the effectiveness for users interested in exactly one relevant item. Despite its widespread use, evide…
Retrieval Augmentation for T5 Re-ranker using External Sources
Kai Hui, Tao Chen, Zhen Qin +4
Retrieval augmentation has shown promising improvements in different tasks. However, whether such augmentation can assist a large language model based re-ranker remains unclear. We…
Retrieval-Enhanced Machine Learning
Hamed Zamani, Fernando Diaz, Mostafa Dehghani +2
Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to inc…
Offline Retrieval Evaluation Without Evaluation Metrics
Fernando Diaz, Andres Ferraro
Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scalar metric such as average precision or no…
Estimation of Fair Ranking Metrics with Incomplete Judgments
Ömer Kırnap, Fernando Diaz, Asia Biega +3
There is increasing attention to evaluating the fairness of search system ranking decisions. These metrics often consider the membership of items to particular groups, often identi…
Overview of the TREC 2020 Fair Ranking Track
Asia J. Biega, Fernando Diaz, Michael D. Ekstrand +2
This paper provides an overview of the NIST TREC 2020 Fair Ranking track. For 2020, we again adopted an academic search task, where we have a corpus of academic article abstracts a…