BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

TL;DR

BEIR is a comprehensive benchmark comprising 18 diverse datasets designed to evaluate the zero-shot generalization of various information retrieval models across different domains and tasks.

Contribution

The paper introduces BEIR, a heterogeneous benchmark for evaluating IR models' out-of-distribution generalization, including a diverse set of datasets and systematic evaluation of multiple retrieval architectures.

Findings

BM25 is a robust baseline.

Re-ranking and late-interaction models perform best but are computationally expensive.

Dense and sparse models are efficient but underperform, indicating room for improvement.

Abstract

Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities. We hope this framework allows us to better evaluate and understand existing retrieval systems, and contributes to accelerating progress towards better robust and generalizable systems in the future. BEIR is publicly available at https://github.com/UKPLab/beir.