Update README.md

README.md

@@ -1,11 +1,16 @@
 ---
-license: mit
-task_categories:
-- question-answering
 language:
 - en
+license: mit
 size_categories:
 - 1M<n<10M
+task_categories:
+- question-answering
+tags:
+- reasoning
+- retrieval
+- synthetic
+- evaluation
 configs:
 - config_name: database
   data_files:
@@ -196,147 +201,55 @@ dataset_info:
 
 # Dataset Card for PhantomWiki
 
-**This repository is a collection of PhantomWiki instances generated using the `phantom-wiki` Python package.**
-
-PhantomWiki is framework for evaluating LLMs, specifically RAG and agentic workflows, that is resistant to memorization.
-Unlike prior work, it is neither a fixed dataset, nor is it based on any existing data.
-Instead, PhantomWiki generates unique dataset instances, comprised of factually consistent document corpora with diverse question-answer pairs, on demand.
+This repository contains pre-generated instances of the PhantomWiki dataset, created using the `phantom-wiki` Python package.  PhantomWiki is a framework for evaluating LLMs, particularly RAG and agentic workflows, designed to be resistant to memorization. Unlike fixed datasets, PhantomWiki generates unique instances on demand, ensuring novelty and preventing data leakage.
 
 ## 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.
+PhantomWiki generates a synthetic fictional universe populated with characters and facts, mirroring the structure of a fan wiki.  These facts are reflected in a large-scale corpus, and diverse question-answer pairs of varying difficulties are then generated.
 
 - **Created by:** Albert Gong, Kamilė Stankevičiūtė, Chao Wan, Anmol Kabra, Raphael Thesmar, Johann Lee, Julius Klenke, Carla P. Gomes, Kilian Q. Weinberger
-- **Funded by:** AG is funded by the NewYork-Presbyterian Hospital; KS is funded by AstraZeneca; CW is funded by NSF OAC-2118310; AK is partially funded by the National Science Foundation (NSF), the National Institute of Food and Agriculture (USDA/NIFA), the Air Force Office of Scientific Research (AFOSR), and a Schmidt AI2050 Senior Fellowship, a Schmidt Sciences program.
-- **Shared by \[optional\]:** \[More Information Needed\]
-- **Language(s) (NLP):** English
+- **Funded by:**  See the [paper](https://huggingface.co/papers/2502.20377) for details.
 - **License:** MIT License
-
-### Dataset Sources \[optional\]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** https://github.com/kilian-group/phantom-wiki
 - **Paper:** [PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation](https://huggingface.co/papers/2502.20377)
-- **Demo:** https://github.com/kilian-group/phantom-wiki/blob/main/demo.ipynb
+- **Code:** [https://github.com/kilian-group/phantom-wiki](https://github.com/kilian-group/phantom-wiki)
 
-## Uses
 
-**We encourage users to generate a new (unique) PhantomWiki instance to combat data leakage and overfitting.**
-PhantomWiki enables quantitative evaluation of the reasoning and retrieval capabilities of LLMs. See our full paper for analysis of frontier LLMs, including GPT-4o, Gemini-1.5-Flash, [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) and [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B).
+### Dataset Sources
 
-### Direct Use
+- **Repository (Code):** [https://github.com/kilian-group/phantom-wiki](https://github.com/kilian-group/phantom-wiki)
 
-PhantomWiki is intended to evaluate retrieval augmented generation (RAG) systems and agentic workflows.
 
-### Out-of-Scope Use
+## Uses
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+PhantomWiki is designed to evaluate retrieval augmented generation (RAG) systems and agentic workflows.  To avoid data leakage and overfitting, generate a new, unique PhantomWiki instance for each evaluation.  Our paper details an analysis of frontier LLMs using PhantomWiki.
 
-## Dataset Structure
+### Dataset Structure
 
-PhantomWiki exposes three components, reflected in the three **configurations**:
+PhantomWiki provides 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. The extent to which we use real data is detailed as follows:
-
-1. We sample surnames from among the most common surnames in the US population according to https://names.mongabay.com/most_common_surnames.htm
-2. We sample first names using the `names` Python package (https://github.com/treyhunner/names). We thank the contributors for making this tool publicly available.
-3. We sample jobs from the list of real-life jobs from the `faker` Python package. We thank the contributors for making this tool publicly available.
-4. We sample hobbies from the list of real-life hobbies at https://www.kaggle.com/datasets/mrhell/list-of-hobbies. We are grateful to the original author for curating this list and making it publicly available.
-
-#### Data Collection and Processing
-
-This dataset was generated on commodity CPUs using Python and SWI-Prolog. See paper for full details of the generation pipeline, including timings.
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-N/A
-
-### Annotations \[optional\]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
 
-N/A
-
-#### Annotation process
-
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-N/A
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-N/A
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-PhantomWiki does not reference any personal or private data.
+Each universe is saved as a separate split.  See the original repository for details on generation and usage.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-PhantomWiki generates large-scale corpora, reflecting fictional universes of characters and mimicking the style of fan-wiki websites. While sufficient for evaluating complex reasoning and retrieval capabilities of LLMs, PhantomWiki is limited to simplified family relations and attributes. Extending the complexity of PhantomWiki to the full scope of Wikipedia is an exciting future direction.
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-PhantomWiki should be used as a benchmark to inform how LLMs should be used on reasoning- and retrieval-based tasks. For holistic evaluation on diverse tasks, PhantomWiki should be combined with other benchmarks.
-
-## Citation \[optional\]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-\[More Information Needed\]
-
-**APA:**
-
-\[More Information Needed\]
-
-## Glossary \[optional\]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-\[More Information Needed\]
-
-## More Information \[optional\]
-
-\[More Information Needed\]
-
-## Dataset Card Authors \[optional\]
+PhantomWiki, while effective at evaluating complex reasoning and retrieval, is limited in its representation of family relations and attributes.  Extending its complexity is a future research direction.  For a holistic evaluation, combine PhantomWiki with other benchmarks.
 
-\[More Information Needed\]
 
-## Dataset Card Contact
+## Citation
 
-[email protected]
+```bibtex
+@article{2025_phantomwiki,
+  title={{PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation}},
+  author={Albert Gong and Kamilė Stankevičiūtė and Chao Wan and Anmol Kabra and Raphael Thesmar and Johann Lee and Julius Klenke and Carla P. Gomes and Kilian Q. Weinberger},
+  year={2025},
+  journal={todo},
+  url={todo},
+  note={Under Review},
+}
+```