---
configs:
- config_name: city_entity
  data_files:
  - split: train
    path: "city_entity/train*.parquet"
  - split: val
    path: "city_entity/val*.parquet"
  - split: test
    path: "city_entity/test*.parquet"
- config_name: city_prompt
  data_files:
  - split: train
    path: "city_prompt/train*.parquet"
  - split: val
    path: "city_prompt/val*.parquet"
  - split: test
    path: "city_prompt/test*.parquet"


license: mit
task_categories:
- text-generation
- question-answering
language:
- en
---
# Dataset Card for RAVEL

<!-- Provide a quick summary of the dataset. -->

A large-scale entity-attribute dataset covering factual, linguistic, and commonsense knowledge.

### To load the dataset:
```python
from datasets import load_dataset

dataset = load_dataset("hij/ravel")
```


## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

The RAVEL dataset contains five types of entities, each with at least 500 instances, at least 4 attributes, and at least 50 prompt templates, as shown in the table below.

|Entity Type|Attributes|\#Entities|\#Prompt Templates|
|---|---|---|---|
|City|Country, Language, Latitude, Longitude,Timezone, Continent|3552|150|
|Nobel Laureate|Award Year, Birth Year, Country of Birth, Field, Gender|928|100|
|Verb|Definition, Past Tense, Pronunciation, Singular | 986 | 60 |
| Physical Object|Biological Category, Color, Size, Texture | 563 | 60 |
|Occupation| Duty, Gender Bias, Industry, Work Location | 799 | 50 |

Compared with existing entity-attribute/relation datasets, such as [CounterFact](https://rome.baulab.info/data/), RAVEL offers two unique features:
* **Multiple attributes** per entity to evaluate how well interpretability methods **isolate individual concepts**
* **x10 more entities** per entity type to evaluate how well interpretability methods **generalize**


### Dataset Sources

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

- **Repository:** [https://github.com/explanare/ravel](https://github.com/explanare/ravel)
- **Paper:** [RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations](https://arxiv.org/pdf/2402.17700)

## Uses

The dataset is primarily designed for interpretability research.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

Each entity type is associated with two subsets: entities and prompt templates.
Both the entities and the prompts are split into train, val, and test.

### Entity

For the entity subset, each example is structured as a dictionary containing the entitiy and attributes. An additional `ID` field is used to disambiguate entities.

For example, the entity type `city` is structured as follows: 
```python
DatasetDict({
    train: Dataset({
        features: ['ID', 'City', 'Continent', 'Country', 'Language', 'Latitude', 'Longitude', 'Timezone', 'URL'],
        num_rows: 2041
    })
    validation: Dataset({
        features: ['ID', 'City', 'Continent', 'Country', 'Language', 'Latitude', 'Longitude', 'Timezone', 'URL'],
        num_rows: 970
    })
    test: Dataset({
        features: ['ID', 'City', 'Continent', 'Country', 'Language', 'Latitude', 'Longitude', 'Timezone', 'URL'],
        num_rows: 1126
    })
})
```

Each example, i.e., an entity, is structured as follows:

```python
{
  "ID": "2498-0",
  "City": "Seattle",
  "Continent": "North America",
  "Country": "United States",
  "Language": "English",
  "Latitude": "48",
  "Longitude": "-122",
  "Timezone": "America/Los_Angeles",
  "URL": "https://en.wikipedia.org/wiki/Seattle"
}
```

### Prompt

The prompt subset contains the prompt templates, which attribute the template is querying, whether this template comes from RAVEL or Wikipedia, and which entities can be used for this template.

An empty string in the `Attribute` field means this prompt is not querying for a specific attribute.

An empty string in the `Entity` field means this prompt can be used with all the entities of the given type.

For example, the prompt templates for `city` are structured as follows:

```python
DatasetDict({
    train: Dataset({
        features: ['Template', 'Attribute', 'Source', 'Entity'],
        num_rows: 442
    })
    val: Dataset({
        features: ['Template', 'Attribute', 'Source', 'Entity'],
        num_rows: 397
    })
    test: Dataset({
        features: ['Template', 'Attribute', 'Source', 'Entity'],
        num_rows: 372
    })
})
```

Each example, i.e., a prompt template, is structured as follows:

```python
{
  'Template': '%s is a city in the country of',
  'Attribute': 'Country',
  'Source': 'RAVEL',
  'Entity': ''
}
```


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```{bibtex}
@inproceedings{huang-etal-2024-ravel,
    title = "{RAVEL}: Evaluating Interpretability Methods on Disentangling Language Model Representations",
    author = "Huang, Jing and Wu, Zhengxuan and Potts, Christopher and Geva, Mor and Geiger, Atticus",
    editor = "Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.470",
    pages = "8669--8687",
}

```