BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models (2104.08663v4)

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.

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

The paper "BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models" presents BEIR (Benchmarking-IR), an evaluation benchmark designed to assess the zero-shot generalization capabilities of information retrieval (IR) models across a wide range of tasks and domains. This benchmark addresses the limitations of prior IR evaluation frameworks by including a diverse collection of 18 datasets spanning various tasks and domains. The paper evaluates ten state-of-the-art retrieval systems, offering insights into their performance under zero-shot conditions.

Key Contributions

1. Heterogeneous Benchmark

The primary contribution of this work is the introduction of a robust, heterogeneous benchmark for IR models. BEIR includes datasets from nine distinct retrieval tasks: fact-checking, citation prediction, duplicate question retrieval, argument retrieval, news retrieval, question answering, tweet retrieval, biomedical IR, and entity retrieval. This diversity enables the comprehensive evaluation of IR models, exposing their strengths and weaknesses across different scenarios.

2. Extensive Model Evaluation

The authors benchmark ten retrieval systems encompassing different architectures: lexical, sparse, dense, late-interaction, and re-ranking:

• Lexical: BM25
• Sparse: DeepCT, SPARTA, docT5query
• Dense: DPR, ANCE, TAS-B, GenQ
• Late-interaction: ColBERT
• Re-ranking: BM25+CE

The evaluation spans various settings, revealing nuanced performance differences among these architectures in zero-shot scenarios. The results show that while simpler, traditional models like BM25 remain competitive, more complex neural architectures often excel given appropriate tasks and domains.

Numerical Results and Key Findings

Comparative Performance

• BM25: Despite being a traditional approach, BM25 exhibits strong baseline performance, outperforming several complex models on certain datasets.
• DeepCT and SPARTA: These models, while performing well in-domain, falter in generalization, frequently underperforming in zero-shot scenarios.
• docT5query: Shows improved generalization by expanding documents, thereby partially overcoming the lexical gap.
• Dense Models: ANCE and TAS-B demonstrate considerable variation in performance, highlighting robustness issues in zero-shot transfer.
• Re-ranking and Late-interaction Models: These, notably BM25+CE and ColBERT, show superior generalization, frequently outperforming other methods across most datasets.

Efficiency Analysis

The paper provides a detailed analysis of model retrieval latency and index sizes, concluding that:

• Retrieval Latency: Dense models are significantly faster than re-ranking and late-interaction models.
• Index Sizes: Lexical, sparse, and dense models have smaller index sizes compared to late-interaction models like ColBERT.

Implications

Comparative Analysis

The diverse evaluation framework of BEIR emphasizes that strong in-domain performance does not necessarily translate to effective zero-shot generalization. This highlights the necessity for broader evaluation metrics and benchmarks, as model robustness in zero-shot settings is crucial for practical applications.

Efficiency Considerations

The efficiency analysis implicates a trade-off between retrieval performance and computational cost. For instance, while re-ranking models offer high accuracy, they come at the expense of increased latency. Conversely, dense models offer faster retrieval times but often underperform complex re-ranking systems.

Future Research Directions

The findings suggest several future research directions:

1. Enhanced Training Mechanisms: Developing training methodologies that better capture the nuances of diverse textual data could improve zero-shot generalization.
2. Balanced Efficiency and Performance: Striking a balance between computational efficiency and retrieval performance remains a critical area for future optimization.
3. Unbiased Dataset Construction: Addressing biases in dataset creation could improve evaluation fairness, offering more reliable comparisons across different retrieval approaches.

Conclusion

The BEIR benchmark sets a new standard for evaluating the zero-shot capabilities of IR models through its comprehensive and diverse dataset collection. The extensive analysis presented in the paper underscores the current limitations and strengths across various retrieval systems, providing valuable insights for developing more robust and generalizable IR solutions. By publicly releasing BEIR, the authors have facilitated ongoing advancements in the IR community, encouraging the standardization of evaluations and fostering innovation in retrieval model development.