CoverageBench: Evaluating Information Coverage across Tasks and Domains
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arXiv
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| Format: | Preprint |
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2026
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| author | Samuel, Saron Yates, Andrew Lawrie, Dawn Soboroff, Ian Adriaanse, Trevor Van Durme, Benjamin Yang, Eugene |
| author_facet | Samuel, Saron Yates, Andrew Lawrie, Dawn Soboroff, Ian Adriaanse, Trevor Van Durme, Benjamin Yang, Eugene |
| contents | We wish to measure the information coverage of an ad hoc retrieval algorithm, that is, how much of the range of available relevant information is covered by the search results. Information coverage is a central aspect for retrieval, especially when the retrieval system is integrated with generative models in a retrieval-augmented generation (RAG) system. The classic metrics for ad hoc retrieval, precision and recall, reward a system as more and more relevant documents are retrieved. However, since relevance in ad hoc test collections is defined for a document without any relation to other documents that might contain the same information, high recall is sufficient but not necessary to ensure coverage. The same is true for other metrics such as rank-biased precision (RBP), normalized discounted cumulative gain (nDCG), and mean average precision (MAP). Test collections developed around the notion of diversity ranking in web search incorporate multiple aspects that support a concept of coverage in the web domain. In this work, we construct a suite of collections for evaluating information coverage from existing collections. This suite offers researchers a unified testbed spanning multiple genres and tasks. All topics, nuggets, relevance labels, and baseline rankings are released on Hugging Face Datasets, along with instructions for accessing the publicly available document collections. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20034 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CoverageBench: Evaluating Information Coverage across Tasks and Domains Samuel, Saron Yates, Andrew Lawrie, Dawn Soboroff, Ian Adriaanse, Trevor Van Durme, Benjamin Yang, Eugene Information Retrieval Artificial Intelligence We wish to measure the information coverage of an ad hoc retrieval algorithm, that is, how much of the range of available relevant information is covered by the search results. Information coverage is a central aspect for retrieval, especially when the retrieval system is integrated with generative models in a retrieval-augmented generation (RAG) system. The classic metrics for ad hoc retrieval, precision and recall, reward a system as more and more relevant documents are retrieved. However, since relevance in ad hoc test collections is defined for a document without any relation to other documents that might contain the same information, high recall is sufficient but not necessary to ensure coverage. The same is true for other metrics such as rank-biased precision (RBP), normalized discounted cumulative gain (nDCG), and mean average precision (MAP). Test collections developed around the notion of diversity ranking in web search incorporate multiple aspects that support a concept of coverage in the web domain. In this work, we construct a suite of collections for evaluating information coverage from existing collections. This suite offers researchers a unified testbed spanning multiple genres and tasks. All topics, nuggets, relevance labels, and baseline rankings are released on Hugging Face Datasets, along with instructions for accessing the publicly available document collections. |
| title | CoverageBench: Evaluating Information Coverage across Tasks and Domains |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2603.20034 |