---
dataset_info:
- config_name: entertainment
  features:
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  - name: question
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  - name: choices
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    - name: B
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    - name: C
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    - name: D
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  - name: answer
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    - name: B
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- config_name: sports
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  - name: choices
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  - name: question
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  - name: choices
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    - name: C
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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)` &rarr; `[Latent Entity]` &rarr; `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]