Jon Saad-Falcon

Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking

Keshav Santhanam*
Jon Saad-Falcon*
Martin Franz
Omar Khattab
Avirup Sil
Radu Florian
Md Arafat Sulton
Salim Roukos
Matei Zaharia
Christopher Potts
Preprint, under review, 2022


Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks. Unfortunately, some dimensions of this progress are illusory: the majority of the popular IR benchmarks today focus exclusively on downstream task accuracy and thus conceal the costs incurred by systems that trade away efficiency for quality. Latency, hardware cost, and other efficiency considerations are paramount to the deployment of IR systems in user-facing settings. We propose that IR benchmarks structure their evaluation methodology to include not only metrics of accuracy, but also efficiency considerations such as a query latency and the corresponding cost budget for a reproducible hardware setting. For the popular IR benchmarks MS MARCO and XOR-TyDi, we show how the best choice of IR system varies according to how these efficiency considerations are chosen and weighed. We hope that future benchmarks will adopt these guidelines toward more holistic IR evaluation.




  title={Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking},
  author={Santhanam, Keshav and Saad-Falcon, Jon and Franz, Martin and Khattab, Omar and Sil, Avirup and Florian, Radu and Sultan, Md Arafat and Roukos, Salim and Zaharia, Matei and Potts, Christopher},
  keywords={Information Retrieval (cs.IR), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information    sciences},
  copyright={Creative Commons Attribution 4.0 International}