---
license: mit
task_categories:
- question-answering
language:
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size_categories:
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configs:
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    path: question-answer/depth_20_size_1000_seed_1-*
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    path: question-answer/depth_20_size_1000_seed_2-*
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    path: question-answer/depth_20_size_5000_seed_1-*
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    path: question-answer/depth_20_size_5000_seed_2-*
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    path: question-answer/depth_20_size_5000_seed_3-*
  - split: depth_20_size_10000_seed_1
    path: question-answer/depth_20_size_10000_seed_1-*
  - split: depth_20_size_10000_seed_2
    path: question-answer/depth_20_size_10000_seed_2-*
  - split: depth_20_size_10000_seed_3
    path: question-answer/depth_20_size_10000_seed_3-*
- config_name: text-corpus
  data_files:
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    path: text-corpus/depth_20_size_25_seed_1-*
  - split: depth_20_size_25_seed_2
    path: text-corpus/depth_20_size_25_seed_2-*
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    path: text-corpus/depth_20_size_25_seed_3-*
  - split: depth_20_size_50_seed_1
    path: text-corpus/depth_20_size_50_seed_1-*
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    path: text-corpus/depth_20_size_50_seed_2-*
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    path: text-corpus/depth_20_size_50_seed_3-*
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    path: text-corpus/depth_20_size_100_seed_1-*
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    path: text-corpus/depth_20_size_100_seed_2-*
  - split: depth_20_size_100_seed_3
    path: text-corpus/depth_20_size_100_seed_3-*
  - split: depth_20_size_200_seed_1
    path: text-corpus/depth_20_size_200_seed_1-*
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    path: text-corpus/depth_20_size_200_seed_2-*
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    path: text-corpus/depth_20_size_200_seed_3-*
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    path: text-corpus/depth_20_size_400_seed_3-*
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    path: text-corpus/depth_20_size_500_seed_1-*
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    path: text-corpus/depth_20_size_500_seed_3-*
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    path: text-corpus/depth_20_size_1000_seed_1-*
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    path: text-corpus/depth_20_size_1000_seed_2-*
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    path: text-corpus/depth_20_size_1000_seed_3-*
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    path: text-corpus/depth_20_size_2500_seed_1-*
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    path: text-corpus/depth_20_size_2500_seed_2-*
  - split: depth_20_size_2500_seed_3
    path: text-corpus/depth_20_size_2500_seed_3-*
  - split: depth_20_size_5000_seed_1
    path: text-corpus/depth_20_size_5000_seed_1-*
  - split: depth_20_size_5000_seed_2
    path: text-corpus/depth_20_size_5000_seed_2-*
  - split: depth_20_size_5000_seed_3
    path: text-corpus/depth_20_size_5000_seed_3-*
  - split: depth_20_size_10000_seed_1
    path: text-corpus/depth_20_size_10000_seed_1-*
  - split: depth_20_size_10000_seed_2
    path: text-corpus/depth_20_size_10000_seed_2-*
  - split: depth_20_size_10000_seed_3
    path: text-corpus/depth_20_size_10000_seed_3-*
---

# Dataset Card for PhantomWiki

**This repository is a collection of PhantomWiki instances generated using the `phantom-wiki` Python package.** 

PhantomWiki is a framework for generating unique, factually consistent document corpora with diverse question-answer pairs. 
Unlike prior work, PhantomWiki is neither a fixed dataset, nor is it based on any existing data. 
Instead, a new PhantomWiki instance is generated on demand for each evaluation.

## Dataset Details

### Dataset Description

PhantomWiki generates a fictional universe of characters along with a set of facts. 
We reflect these facts in a large-scale corpus, mimicking the style of fan-wiki websites. 
Then we generate question-answer pairs with tunable difficulties, encapsulating the types of multi-hop questions commonly considered in the question-answering (QA) literature.


- **Curated by:** Albert Gong, Kamilė Stankevičiūtė, Chao Wan, Anmol Kabra, Raphael Thesmar, Johann Lee, Julius Klenke, Carla P. Gomes, Kilian Q. Weinberger
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** Apache License 2.0

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/albertgong1/phantom-wiki
- **Paper [optional]:** TODO
- **Demo [optional]:** [More Information Needed]

## Uses

PhantomWiki is intended to evaluate retrieval augmented generation (RAG) systems and agentic workflows.

### Direct Use

Owing to its fully synthetic and controllable nature, PhantomWiki is particularly useful for disentangling the reasoning and retrieval capabilities of large language models.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

PhantomWiki exposes three components, reflected in the three **configurations**:
1. `question-answer`: Question-answer pairs generated using a context-free grammar
2. `text-corpus`: Documents generated using natural-language templates
3. `database`: Prolog database containing the facts and clauses representing the universe

Each universe is saved as a **split**.

## Dataset Creation

### Curation Rationale

Most mathematical and logical reasoning datasets do not explicity evaluate retrieval capabilities and 
few retrieval datasets incorporate complex reasoning, save for a few exceptions (e.g., [BRIGHT](https://huggingface.co/datasets/xlangai/BRIGHT), [MultiHop-RAG](https://huggingface.co/datasets/yixuantt/MultiHopRAG)). 
However, virtually all retrieval datasets are derived from Wikipedia or internet articles, which are contained in LLM training data.
We take the first steps toward a large-scale synthetic dataset that can evaluate LLMs' reasoning and retrieval capabilities.

### Source Data

This is a synthetic dataset.

#### Data Collection and Processing

This dataset was generated on commodity CPUs using Python and Prolog. See paper for full details of the generation pipeline, including timings. 

#### Who are the source data producers?

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### Annotations [optional]

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#### Annotation process

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#### Who are the annotators?

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#### Personal and Sensitive Information

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## Bias, Risks, and Limitations

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### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Dataset Card Authors [optional]

Albert Gong

## Dataset Card Contact

agong@cs.cornell.edu