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| "description": "\n\t\n\t\t\n\t\tShadowBench: A Hardened Benchmark for Latent Entity Association\n\t\n\nShadowBench is a diagnostic framework designed to evaluate the \"Shadow Knowledge\" of Large Language Models (LLMs). While traditional benchmarks measure factual recall using explicit entity names (e.g., \"Elon Musk\"), ShadowBench evaluates whether a model can navigate its internal knowledge graph when these lexical anchors are removed.\n\n\t\n\t\t\n\t\n\t\n\t\tDataset Summary\n\t\n\nThe core task in ShadowBench is Dual-Trait Association… See the full description on the dataset page: https://huggingface.co/datasets/shadow-bench/ShadowBench.",
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| "question-answering",
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| "rai:dataBiases": "The dataset contains an intentional popularity bias used to evaluate the 'Knowledge Cliff' phenomenon. There is a potential demographic skew reflecting Wikipedia's historical representation patterns, primarily favoring Western-centric public figures.",
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| "rai:personalSensitiveInformation": "The dataset contains names and biographical facts of public figures (celebrities, tech executives, athletes). It does not contain private PII, non-public contact information, medical data, or financial records.",
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| "rai:dataSocialImpact": "ShadowBench aims to improve AI safety by exposing vulnerabilities in privacy-preserving unlearning methods. It highlights the risk of 'Superficial Forgetting' where sensitive information remains retrievable via latent associations.",
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| "name": "Entity Discovery and Stratification",
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| "description": "Automated Breadth-First Search (BFS) traversal of English Wikipedia category graphs to identify candidate entities. Entities were ranked and stratified into Upper and Lower tiers using a multimodal popularity score (Sp).",
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| "name": "Factual Mining and Hardening (v1-v3)",
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| "description": "Extraction of factual traits from the January 1, 2023 Wikipedia snapshot. Applied spaCy-based NER filtering for attribute density. Implemented an iterative hardening pipeline including lexical anonymization, pronoun neutralization, and the application of a 25-year Generational Proximity Filter (GPF).",
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| "wasAssociatedWith": {
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| "name": "Adversarial MCQ Synthesis",
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| "description": "Combinatorial permutation of traits (Trait A to Trait B) with gender-homogeneous hard-negative distractor matching to prevent non-semantic shortcut learning.",
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| "wasAssociatedWith": {
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| "@type": "prov:SoftwareAgent",
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| "name": "ShadowBench MCQ Generator"
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| "@type": "prov:Activity",
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| "name": "Human-in-the-loop Audit",
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| "description": "Manual quality review by the research team to ensure bijective mapping (uniqueness) of shadow descriptions and factual accuracy across all 7,000+ QA pairs.",
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|
|
