size_categories:100K<n<1M format:parquet modality:text library:datasets library:dask library:mlcroissant library:polars region:us
---
dataset_info:
- config_name: Multihop
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: replaced_answer
    dtype: string
  - name: conflict_time_span
    sequence: string
  - name: subject
    dtype: string
  - name: object
    dtype: string
  - name: subject_additional_information
    dtype: string
  - name: object_additional_information
    dtype: string
  - name: replaced_object_additional_information
    dtype: string
  - name: correct_evidence_blog
    dtype: string
  - name: correct_evidence_wikipedia
    dtype: string
  - name: correct_evidence_news_report
    dtype: string
  - name: correct_evidence_science_reference
    dtype: string
  - name: correct_evidence_confident_language
    dtype: string
  - name: correct_evidence_technical_language
    dtype: string
  - name: fact_conflict_evidence_blog
    dtype: string
  - name: fact_conflict_evidence_wikipedia
    dtype: string
  - name: fact_conflict_evidence_news_report
    dtype: string
  - name: fact_conflict_evidence_science_reference
    dtype: string
  - name: fact_conflict_evidence_confident_language
    dtype: string
  - name: fact_conflict_evidence_technical_language
    dtype: string
  - name: temporal_conflict_evidence_blog
    dtype: string
  - name: temporal_conflict_evidence_wikipedia
    dtype: string
  - name: temporal_conflict_evidence_news_report
    dtype: string
  - name: temporal_conflict_evidence_science_reference
    dtype: string
  - name: temporal_conflict_evidence_confident_language
    dtype: string
  - name: temporal_conflict_evidence_technical_language
    dtype: string
  - name: semantic_conflict_evidence_blog
    dtype: string
  - name: semantic_conflict_evidence_wikipedia
    dtype: string
  - name: semantic_conflict_evidence_news_report
    dtype: string
  - name: semantic_conflict_evidence_science_reference
    dtype: string
  - name: semantic_conflict_evidence_confident_language
    dtype: string
  - name: semantic_conflict_evidence_technical_language
    dtype: string
  splits:
  - name: train
    num_bytes: 4536874111
    num_examples: 83221
  download_size: 2365091922
  dataset_size: 4536874111
- config_name: Onehop
  features:
  - name: relation
    dtype: string
  - name: subject
    dtype: string
  - name: object
    dtype: string
  - name: replaced_object
    dtype: string
  - name: correct_pair
    dtype: string
  - name: fact_conflict_pair
    dtype: string
  - name: temporal_conflict_pair
    dtype: string
  - name: conflict_time_span
    sequence: string
  - name: subject_additional_information
    dtype: string
  - name: object_additional_information
    dtype: string
  - name: replaced_object_additional_information
    dtype: string
  - name: question
    dtype: string
  - name: correct_evidence_blog
    dtype: string
  - name: correct_evidence_wikipedia
    dtype: string
  - name: correct_evidence_news_report
    dtype: string
  - name: correct_evidence_science_reference
    dtype: string
  - name: correct_evidence_confident_language
    dtype: string
  - name: correct_evidence_technical_language
    dtype: string
  - name: fact_conflict_evidence_blog
    dtype: string
  - name: fact_conflict_evidence_wikipedia
    dtype: string
  - name: fact_conflict_evidence_news_report
    dtype: string
  - name: fact_conflict_evidence_science_reference
    dtype: string
  - name: fact_conflict_evidence_confident_language
    dtype: string
  - name: fact_conflict_evidence_technical_language
    dtype: string
  - name: semantic_conflict_evidence_blog
    dtype: string
  - name: semantic_conflict_evidence_wikipedia
    dtype: string
  - name: semantic_conflict_evidence_news_report
    dtype: string
  - name: semantic_conflict_evidence_science_reference
    dtype: string
  - name: semantic_conflict_evidence_confident_language
    dtype: string
  - name: semantic_conflict_evidence_technical_language
    dtype: string
  splits:
  - name: train
    num_bytes: 14845432329
    num_examples: 347892
  download_size: 6888525278
  dataset_size: 14845432329
configs:
- config_name: Multihop
  data_files:
  - split: train
    path: Multihop/train-*
- config_name: Onehop
  data_files:
  - split: train
    path: Onehop/train-*
---

# Dataset Card for MisBench

## Dataset Summary

**MisBench** is a comprehensive benchmark for evaluating LLMs' behavior and knowledge preference toward misinformation, including **10,346,712** pieces of misinformation across **3** types and **6** textual styles (e.g., news reports, blogs, and technical language) spanning 12 domains.


## Subsets
1. **Onehop Subset**: Contains 348k rows, focusing on single-hop QA pairs and corresponding misinformation.
2. **Multihop Subset**: Includes 83.2k rows, focusing on multi-hop QA pairs and corresponding misinformation.


### Data Statistics:
- **Rows**: 431,113
- **Fields**: 20+ structured fields
- **Misinformation Types**: Factual misinformation, temporal misinformation, and semantic misinformation.
- **Evidence Styles**: Blog posts, Wikipedia entry, news reports, scientific references, technical language, and scientific language.


## Dataset Structure

The dataset contains the following fields:
- `question` (string): The input question or claim to be verified.
- `answer` (string): The correct answer to the question.
- `replaced_answer` (string): Modified or misleading answer to simulate misinformation.
- `conflict_time_span` (sequence): Temporal conflict indicators.
- `subject` and `object` (strings): Entities involved in the claim.
- Additional metadata fields such as `correct_evidence_blog`, `fact_conflict_evidence_wikipedia`, `temporal_conflict_evidence_news_report`, etc., contain generated evidence in various types and styles to support or refute the claim.


## Data Fields

Here is a brief description of the key fields:
- `question`: The question or claim to be verified.
- `answer`: The correct answer to the question.
- `replaced_answer`: Misinformation or alternative answers.
- `subject` / `object`: Entities in the claim.
- Evidence fields:
  - `correct_evidence_*` (e.g., Wikipedia, blog posts, etc.)
  - `fact_conflict_evidence_*` (e.g., Wikipedia, blog posts, etc.)
  - `temporal_conflict_evidence_*` (e.g., Wikipedia, blog posts, etc.)
  - `semantic_conflict_evidence_*` (e.g., Wikipedia, blog posts, etc.)

## Usage

### Example Usage in Python
```python
from datasets import load_dataset

# Load the MisBench dataset
dataset = load_dataset("MisBench/MisBench", split="train")

# Explore the first row
print(dataset[0])