| --- |
| dataset_info: |
| - config_name: entertainment |
| features: |
| - name: entity |
| dtype: string |
| - name: question |
| dtype: string |
| - name: choices |
| struct: |
| - name: A |
| dtype: string |
| - name: B |
| dtype: string |
| - name: C |
| dtype: string |
| - name: D |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: metadata |
| struct: |
| - name: A |
| dtype: string |
| - name: B |
| dtype: string |
| - name: C |
| dtype: string |
| - name: D |
| dtype: string |
| splits: |
| - name: upper_shadow |
| num_bytes: 990469 |
| num_examples: 1164 |
| - name: lower_shadow |
| num_bytes: 723122 |
| num_examples: 1154 |
| - name: upper_shadow_controlled |
| num_bytes: 146264 |
| num_examples: 172 |
| - name: lower_shadow_controlled |
| num_bytes: 109891 |
| num_examples: 172 |
| - name: upper_direct |
| num_bytes: 993682 |
| num_examples: 1164 |
| - name: lower_direct |
| num_bytes: 727278 |
| num_examples: 1154 |
| download_size: 686647 |
| dataset_size: 3690706 |
| - config_name: sports |
| features: |
| - name: entity |
| dtype: string |
| - name: question |
| dtype: string |
| - name: choices |
| struct: |
| - name: A |
| dtype: string |
| - name: B |
| dtype: string |
| - name: C |
| dtype: string |
| - name: D |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: metadata |
| struct: |
| - name: A |
| dtype: string |
| - name: B |
| dtype: string |
| - name: C |
| dtype: string |
| - name: D |
| dtype: string |
| splits: |
| - name: upper_shadow |
| num_bytes: 851770 |
| num_examples: 1030 |
| - name: lower_shadow |
| num_bytes: 718409 |
| num_examples: 1012 |
| - name: upper_shadow_controlled |
| num_bytes: 85285 |
| num_examples: 104 |
| - name: lower_shadow_controlled |
| num_bytes: 67686 |
| num_examples: 104 |
| - name: upper_direct |
| num_bytes: 855263 |
| num_examples: 1030 |
| - name: lower_direct |
| num_bytes: 723358 |
| num_examples: 1012 |
| download_size: 525434 |
| dataset_size: 3301771 |
| - config_name: technology |
| features: |
| - name: entity |
| dtype: string |
| - name: question |
| dtype: string |
| - name: choices |
| struct: |
| - name: A |
| dtype: string |
| - name: B |
| dtype: string |
| - name: C |
| dtype: string |
| - name: D |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: metadata |
| struct: |
| - name: A |
| dtype: string |
| - name: B |
| dtype: string |
| - name: C |
| dtype: string |
| - name: D |
| dtype: string |
| splits: |
| - name: upper_shadow |
| num_bytes: 1063796 |
| num_examples: 1370 |
| - name: lower_shadow |
| num_bytes: 999160 |
| num_examples: 1344 |
| - name: upper_shadow_controlled |
| num_bytes: 215759 |
| num_examples: 282 |
| - name: lower_shadow_controlled |
| num_bytes: 201445 |
| num_examples: 282 |
| - name: upper_direct |
| num_bytes: 1063797 |
| num_examples: 1370 |
| - name: lower_direct |
| num_bytes: 1004136 |
| num_examples: 1344 |
| download_size: 1065847 |
| dataset_size: 4548093 |
| configs: |
| - config_name: entertainment |
| data_files: |
| - split: upper_shadow |
| path: entertainment/upper_shadow-* |
| - split: lower_shadow |
| path: entertainment/lower_shadow-* |
| - split: upper_shadow_controlled |
| path: entertainment/upper_shadow_controlled-* |
| - split: lower_shadow_controlled |
| path: entertainment/lower_shadow_controlled-* |
| - split: upper_direct |
| path: entertainment/upper_direct-* |
| - split: lower_direct |
| path: entertainment/lower_direct-* |
| - config_name: sports |
| data_files: |
| - split: upper_shadow |
| path: sports/upper_shadow-* |
| - split: lower_shadow |
| path: sports/lower_shadow-* |
| - split: upper_shadow_controlled |
| path: sports/upper_shadow_controlled-* |
| - split: lower_shadow_controlled |
| path: sports/lower_shadow_controlled-* |
| - split: upper_direct |
| path: sports/upper_direct-* |
| - split: lower_direct |
| path: sports/lower_direct-* |
| - config_name: technology |
| data_files: |
| - split: upper_shadow |
| path: technology/upper_shadow-* |
| - split: lower_shadow |
| path: technology/lower_shadow-* |
| - split: upper_shadow_controlled |
| path: technology/upper_shadow_controlled-* |
| - split: lower_shadow_controlled |
| path: technology/lower_shadow_controlled-* |
| - split: upper_direct |
| path: technology/upper_direct-* |
| - split: lower_direct |
| path: technology/lower_direct-* |
| license: cc-by-sa-4.0 |
| task_categories: |
| - question-answering |
| - multiple-choice |
| language: |
| - en |
| tags: |
| - knowledge-probing |
| - llm-evaluation |
| - entity-resolution |
| - machine-unlearning |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # ShadowBench: A Hardened Benchmark for Latent Entity Association |
|
|
| **ShadowBench** 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. |
|
|
| ## Dataset Summary |
| The core task in ShadowBench is **Dual-Trait Association (DTA)**. A model is presented with an anonymized shadow description (Trait A) and must associate it with a second, independent fact (Trait B) among three "Hard Negative" distractors. |
|
|
| Success requires the model to utilize the hidden entity as a semantic bridge: |
|
|
| `Trait A (Shadow)` → `[Latent Entity]` → `Trait B (Target Choice)` |
|
|
| ### Key Features |
| * **Adversarially Hardened:** Unlike standard MCQs, ShadowBench (v3) is filtered to prevent "shortcut learning" via gendered pronouns, chronological era-matching, or category-leaks. |
| * **Scale Robust:** Evaluated on models ranging from 8B parameters (Llama-3, Qwen3) to frontier scales (GPT-5.4-mini, GPT-5.4, and Claude-Sonnet-4.6). |
| * **Multi-Domain:** Covers Technology, Sports (Tennis), and Entertainment (Actors). |
| * **Stratified:** Includes "Upper Tier" (Head) and "Lower Tier" (Tail) entities based on Wikipedia popularity metrics to evaluate "Popularity Bias." |
|
|
| ## Dataset Structure |
|
|
| ### Subsets |
| The dataset is divided into three primary domains: |
| * `technology`: Corporate, product, and leadership-based associations. |
| * `sports`: Numerical achievements and career milestones in professional tennis. |
| * `entertainment`: Narrative roles and filmographic associations. |
|
|
| ### Splits |
| Each subset contains the following splits: |
| * `upper_shadow` / `lower_shadow`: The primary anonymized DTA task. |
| * `upper_direct` / `lower_direct`: A control split where explicit names are restored to establish a factual "ceiling" (Direct QA). |
| * `upper_controlled` / `lower_controlled`: A 1:1 entity-matched subset used for sensitivity analysis. |
|
|
| ### Data Schema |
| Each sample contains: |
| - `entity`: The hidden entity name. |
| - `question`: The shadow description (Trait A). |
| - `choices`: A dictionary (A, B, C, D) containing Trait B and three hard distractors. |
| - `answer`: The correct option key. |
| - `metadata`: A mapping dictionary where each key (A, B, C, D) corresponds to the actual entity represented by that answer choice. |
|
|
| ## Construction & Hardening (v1 to v3) |
| ShadowBench was developed through an iterative process to ensure success is strictly contingent on latent semantic reasoning: |
| 1. **v1:** Lexical Anonymization (Names removed). |
| 2. **v2:** Chronological & Syntactic Hardening (Pronouns neutralized + Generational Proximity Filter added). |
| 3. **v3:** Demographic Homogeneity (Gender-matched distractors added to prevent elimination via lexical cues like "WTA" or "Best Actress"). |
|
|
| ## Usage |
| You can load this dataset using the Hugging Face `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the Technology Shadow split |
| dataset = load_dataset("shadow-bench/ShadowBench", "technology", split="upper_shadow") |
| |
| # Inspect a sample |
| print(dataset[0]) |
| ``` |
|
|
| ## Licensing |
|
|
| This dataset is derived from Wikipedia and is licensed under CC BY-SA 4.0. |
|
|
| ## Citation |
|
|
| If you use this dataset in your research, please cite our paper: [TBD] |
