Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard (2306.07471v1)

Abstract: BEIR is a benchmark dataset for zero-shot evaluation of information retrieval models across 18 different domain/task combinations. In recent years, we have witnessed the growing popularity of a representation learning approach to building retrieval models, typically using pretrained transformers in a supervised setting. This naturally begs the question: How effective are these models when presented with queries and documents that differ from the training data? Examples include searching in different domains (e.g., medical or legal text) and with different types of queries (e.g., keywords vs. well-formed questions). While BEIR was designed to answer these questions, our work addresses two shortcomings that prevent the benchmark from achieving its full potential: First, the sophistication of modern neural methods and the complexity of current software infrastructure create barriers to entry for newcomers. To this end, we provide reproducible reference implementations that cover the two main classes of approaches: learned dense and sparse models. Second, there does not exist a single authoritative nexus for reporting the effectiveness of different models on BEIR, which has led to difficulty in comparing different methods. To remedy this, we present an official self-service BEIR leaderboard that provides fair and consistent comparisons of retrieval models. By addressing both shortcomings, our work facilitates future explorations in a range of interesting research questions that BEIR enables.

Overview of "Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard"

This paper addresses two critical shortcomings in the BEIR benchmark ecosystem, which evaluates zero-shot retrieval models across diverse tasks and domains. The authors provide reproducible reference implementations for retrieval methods and introduce an official leaderboard, significantly enhancing accessibility and comparisons among different models.

Key Contributions

1. Reproducible Implementations: The authors present reproducible reference implementations for five popular retrieval models within the Pyserini toolkit. These models represent both dense and sparse approaches, allowing researchers to conduct end-to-end retrieval runs with minimal setup effort.
2. Official Leaderboard: An official, community-driven leaderboard for BEIR is introduced, replacing previous informal and static methods for result sharing. Hosted on the EvalAI platform, it allows for consistent and accurate comparisons across various models and datasets.
3. Visualization Methodology: The paper also introduces radar charts for visually comparing models' effectiveness across datasets, highlighting gains and losses at a glance.
4. Analysis of Model Variants: Experiments explore the effect of various approaches, such as multi-field indexing, sentence-based document segmentation, and hybrid model fusion, on model performance.

BEIR Benchmark

BEIR includes 18 datasets covering a wide range of tasks and domains, such as ad hoc retrieval, question answering, and fact-checking. The benchmark is designed to test out-of-distribution generalization, crucial for evaluating retrieval models' adaptability across different types of data.

Model Implementations

• Dense Models: TAS-B and Contriever, both leveraging BERT-based architectures, are used to examine the effectiveness of dense semantic representation in retrieval tasks.
• Sparse Models: SPLADE and uniCOIL explore sparse lexical representations using transformer networks.
• BM25 Baseline: A strong lexical baseline using multi-field indexing for comparative analysis.

Main Findings

The paper reveals that dense and sparse models exhibit inconsistent zero-shot performance across BEIR datasets. SPLADE consistently outperforms on several datasets, but challenges remain, particularly in domain-specific collections such as BioASQ and TREC-COVID.

The radar chart visualizations effectively highlight these discrepancies, providing valuable insights into where each model excels or underperforms compared to the BM25 baseline.

Hybrid Models

Hybrid fusion of dense and sparse representations, particularly combining Contriever and SPLADE, shows promise in achieving robust performance across varied datasets. This approach capitalizes on the strengths of each model type, yielding consistent improvements over standalone models.

Conclusion and Future Work

The paper advances the utility of the BEIR benchmark by ensuring reproducibility and consistent result sharing across the research community. Future challenges include the need for standardized significance testing across aggregated datasets and exploring systematic biases in relevance judgments within BEIR.

Overall, these efforts encourage transparent and rigorous evaluations of retrieval models, fostering advancements in the field of information retrieval.