Information retrieval evaluation often suffers from fragmented practices -- varying dataset subsets, aggregation methods, and pipeline configurations -- that undermine reproducibility and comparability, especially for foundation embedding models requiring robust out-of-domain performance. We introduce SuiteEval, a unified framework that offers automatic end-to-end evaluation, dynamic indexing that reuses on-disk indices to minimise disk usage, and built-in support for major benchmarks (BEIR, LoTTE, MS MARCO, NanoBEIR, and BRIGHT). Users only need to supply a pipeline generator. SuiteEval handles data loading, indexing, ranking, metric computation, and result aggregation. New benchmark suites can be added in a single line. SuiteEval reduces boilerplate and standardises evaluations to facilitate reproducible IR research, as a broader benchmark set is increasingly required.
@article{arxiv.2602.18107,
title = {SuiteEval: Simplifying Retrieval Benchmarks},
author = {Andrew Parry and Debasis Ganguly and Sean MacAvaney},
journal= {arXiv preprint arXiv:2602.18107},
year = {2026}
}
Comments
5 pages, 3 figures, 2 tables, Accepted as a Demonstration to ECIR 2026