CoverageBench: Evaluating Information Coverage across Tasks and Domains

• URL: http://arxiv.org/abs/2603.20034v1
• Date: Fri, 20 Mar 2026 15:20:44 GMT
• Title: CoverageBench: Evaluating Information Coverage across Tasks and Domains
• Abstract summary: Information coverage is a central aspect for retrieval, especially when the retrieval system is integrated with generative models.<n>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.
• Score: 57.33043453870487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Query Decomposition for RAG: Balancing Exploration-Exploitation [83.79639293409802]
  RAG systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggregating them to generate an answer.<n>We formulate query decomposition and document retrieval in an exploitation-exploration setting, where retrieving one document at a time builds a belief about the utility of a given sub-queries.<n>Our main finding is that estimating document relevance using rank information and human judgments yields a 35% gain in document-level precision, 15% increase in alpha-nDCG, and better performance on the downstream task of long-form generation.
  arXiv  Detail & Related papers  (2025-10-21T13:37:11Z)
• Learning Refined Document Representations for Dense Retrieval via Deliberate Thinking [58.69615583599489]
  Deliberate Thinking based Retriever (Debater) is a novel approach that enhances document representations by incorporating a step-by-step thinking process.<n>Debater significantly outperforms existing methods across several retrieval benchmarks.
  arXiv  Detail & Related papers  (2025-02-18T15:56:34Z)
• Generative Retrieval for Book search [106.67655212825025]
  We propose an effective Generative retrieval framework for Book Search.<n>It features two main components: data augmentation and outline-oriented book encoding.<n>Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-01-19T12:57:13Z)
• Generative Retrieval Meets Multi-Graded Relevance [104.75244721442756]
  We introduce a framework called GRaded Generative Retrieval (GR$2$) GR$2$ focuses on two key components: ensuring relevant and distinct identifiers, and implementing multi-graded constrained contrastive training. Experiments on datasets with both multi-graded and binary relevance demonstrate the effectiveness of GR$2$.
  arXiv  Detail & Related papers  (2024-09-27T02:55:53Z)
• Open-World Evaluation for Retrieving Diverse Perspectives [39.22331280176582]
  We curate a Benchmark for Retrieval Diversity for Subjective questions (BERDS)<n>Each example consists of a question and diverse perspectives associated with the question.<n>We build a language model-based automatic evaluator that decides whether each retrieved document contains a perspective.
  arXiv  Detail & Related papers  (2024-09-26T17:52:57Z)
• Evaluating Generative Ad Hoc Information Retrieval [58.800799175084286]
  generative retrieval systems often directly return a grounded generated text as a response to a query. Quantifying the utility of the textual responses is essential for appropriately evaluating such generative ad hoc retrieval.
  arXiv  Detail & Related papers  (2023-11-08T14:05:00Z)
• Beyond Document Page Classification: Design, Datasets, and Challenges [32.94494070330065]
  This paper highlights the need to bring document classification benchmarking closer to real-world applications. We identify the lack of public multi-page document classification datasets, formalize different classification tasks arising in application scenarios, and motivate the value of targeting efficient multi-page document representations.
  arXiv  Detail & Related papers  (2023-08-24T16:16:47Z)
• DAPR: A Benchmark on Document-Aware Passage Retrieval [57.45793782107218]
  We propose and name this task emphDocument-Aware Passage Retrieval (DAPR) While analyzing the errors of the State-of-The-Art (SoTA) passage retrievers, we find the major errors (53.5%) are due to missing document context. Our created benchmark enables future research on developing and comparing retrieval systems for the new task.
  arXiv  Detail & Related papers  (2023-05-23T10:39:57Z)
• Cross-document Event Coreference Search: Task, Dataset and Modeling [26.36068336169796]
  We propose an appealing, and often more applicable, complementary set up for the task - Cross-document Coreference Search. To support research on this task, we create a corresponding dataset, which is derived from Wikipedia. We present a novel model that integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.
  arXiv  Detail & Related papers  (2022-10-23T08:21:25Z)
• CODER: An efficient framework for improving retrieval through COntextualized Document Embedding Reranking [11.635294568328625]
  We present a framework for improving the performance of a wide class of retrieval models at minimal computational cost. It utilizes precomputed document representations extracted by a base dense retrieval method. It incurs a negligible computational overhead on top of any first-stage method at run time, allowing it to be easily combined with any state-of-the-art dense retrieval method.
  arXiv  Detail & Related papers  (2021-12-16T10:25:26Z)
• Exposing Query Identification for Search Transparency [69.06545074617685]
  We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems. We derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.
  arXiv  Detail & Related papers  (2021-10-14T20:19:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.