analiza aiqbench
The paper introduces CoverageBench-Negate, a benchmark that tests whether retrievers surface corpus evidence when it contradicts a model's prior knowledge. Its baseline result is notable: BM25 separates original from edited evidence well, while dense retrievers such as Qwen3 and Jina perform near chance. For readers building retrieval-augmented systems, this points to a real weakness in embedding-based search when sources conflict with what a model already "knows," and the released dataset and prompts make the claim testable.
Coverage Under Contradiction: A Benchmark for Negation-Aware Retrieval
arXiv:2610.37162v1 Announce Type: cross Abstract: Coverage-evaluation benchmarks quantify whether retrieved evidence covers the different pieces of information needed to satisfy an information need. Existing benchmarks mostly consider factual claims that may already be known to pretrained retrieval components, leaving open whether retrievers find corpus evidence when it contradicts those priors. We study how retrievers behave when their parametric knowledge and the information in a corpus disagree. To do so, we develop a process to augment a nugget-based coverage benchmark with controlled contradictions. For each nugget, we create a negated counterpart and edit its supporting documents to support that negation while preserving the context. Applying this process to CoverageBench, a benchmark measuring information coverage, we create CoverageBench-Negate, spanning six datasets and roughly 63,000 original-edited evidence pairs (one per nugget-document link). We attach rich construction metadata to every pair and additionally provide out-of-family LLM judgments over all pairs and a human audit of a sample. In baseline experiments, BM25 separates original from edited near-twin evidence substantially better than dense retrievers (Qwen3 and Jina), which stay near chance. We release the benchmark on Hugging Face Datasets (nuggets, documents, pairs) together with the construction materials, including the prompts used to create it.
Abstract:Coverage-evaluation benchmarks quantify whether retrieved evidence covers the different pieces of information needed to satisfy an information need. Existing benchmarks mostly consider factual claims that may already be known to pretrained retrieval components, leaving open whether retrievers find corpus evidence when it contradicts those priors. We study how retrievers behave when their parametric knowledge and the information in a corpus disagree. To do so, we develop a process to augment a nugget-based coverage benchmark with controlled contradictions. For each nugget, we create a negated counterpart and edit its supporting documents to support that negation while preserving the context. Applying this process to CoverageBench, a benchmark measuring information coverage, we create CoverageBench-Negate, spanning six datasets and roughly 63,000 original-edited evidence pairs (one per nugget-document link). We attach rich construction metadata to every pair and additionally provide out-of-family LLM judgments over all pairs and a human audit of a sample. In baseline experiments, BM25 separates original from edited near-twin evidence substantially better than dense retrievers (Qwen3 and Jina), which stay near chance. We release the benchmark on Hugging Face Datasets (nuggets, documents, pairs) together with the construction materials, including the prompts used to create it.
5 pages, 1 figure. Accepted at CIKM 2026
Information Retrieval (cs.IR); Computation and Language (cs.CL)
arXiv:2610.37162 [cs.IR]
(or arXiv:2610.37162v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2610.37162 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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