Datasets:

shadow-bench
/

ShadowBench

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
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        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
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        num_examples: 104
      - name: lower_shadow_controlled
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        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
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        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:

v1: Lexical Anonymization (Names removed).
v2: Chronological & Syntactic Hardening (Pronouns neutralized + Generational Proximity Filter added).
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:

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]