Several benchmarks exist to evaluate semantic search systems, focusing on tasks like relevance ranking, query-document matching, and cross-domain generalization. Commonly used benchmarks include MS MARCO, BEIR, TREC Deep Learning Track, Semantic Textual Similarity (STS), and domain-specific datasets like Natural Questions (NQ) or HotpotQA. These benchmarks test how well models understand queries, retrieve contextually relevant results, and handle diverse data types (e.g., short paragraphs, long documents, or multi-hop reasoning). For example, MS MARCO uses real-world Bing search queries and human-annotated passages, while BEIR aggregates 15+ datasets to measure zero-shot generalization. Each benchmark defines specific evaluation metrics and tasks, such as ranking accuracy or similarity scoring, to quantify performance.
MS MARCO is a widely adopted benchmark for large-scale semantic search, focusing on passage ranking and question answering. It uses Mean Reciprocal Rank (MRR@10) to measure how well a system places the correct answer in the top 10 results. BEIR, on the other hand, evaluates models across diverse domains (e.g., biomedical, legal) using metrics like nDCG@10, testing whether a model trained on one dataset can generalize to others. The TREC Deep Learning Track provides structured evaluation campaigns with tasks like document retrieval for complex queries, often using precision-focused metrics. For similarity-based tasks, STS benchmarks like STS-B or STS-17 use Pearson correlation to score how well models align human-rated text similarity. Datasets like Natural Questions focus on open-domain QA, where systems must retrieve exact answers from Wikipedia passages, measured by exact match accuracy. HotpotQA adds complexity by requiring multi-step reasoning across multiple documents, testing both answer correctness and supporting evidence retrieval.
When selecting a benchmark, developers should consider their use case’s requirements. For general-purpose search engines, BEIR’s multi-dataset approach helps assess robustness across domains. If the goal is to optimize for real-world web search, MS MARCO’s large-scale query-passage pairs are more relevant. Domain-specific applications (e.g., medical or legal search) may require custom datasets or subsets like BioASQ or LegalBench. Task type also matters: STS suits applications measuring similarity (e.g., duplicate detection), while HotpotQA is better for systems needing explainability through multi-hop reasoning. Additionally, evaluation metrics should align with business goals—nDCG prioritizes ranking quality, while MRR focuses on top-result accuracy. Finally, computational constraints matter; large benchmarks like MS MARCO demand significant infrastructure, whereas smaller datasets (e.g., STS) allow rapid iteration. Choosing the right benchmark ensures meaningful insights into a model’s strengths and weaknesses in real-world scenarios.