size_categories:100K<n<1M language:en not-for-all-audiences arxiv:2309.00779 arxiv:2304.03738 region:us
---
configs:
- config_name: full
  data_files: full/*csv
  default: true
- config_name: mixture
  data_files:
  - split: train
    path: mixture/*train.csv
  - split: val
    path: mixture/*val.csv
  - split: test
    path: mixture/*test.csv
- config_name: generative
  data_files:
  - split: train
    path: generative/*train.csv
  - split: val
    path: generative/*val.csv
  - split: test
    path: generative/*test.csv
- config_name: relevance
  data_files:
  - split: train
    path: relevance/*train.csv
  - split: val
    path: relevance/*val.csv
  - split: test
    path: relevance/*test.csv
- config_name: explanation
  data_files:
  - split: train
    path: explanation/*train.csv
  - split: val
    path: explanation/*val.csv
  - split: test
    path: explanation/*test.csv
- config_name: valence
  data_files:
  - split: train
    path: valence/*train.csv
  - split: val
    path: valence/*val.csv
  - split: test
    path: valence/*test.csv
annotations_creators:
- crowdsourced: null
  machine-generated: null
language:
- en
pretty_name: ValuePrism
extra_gated_prompt: >-
  Access to this dataset is automatically granted upon accepting the [**AI2
  ImpACT License - Medium Risk Artifacts (“MR
  Agreement”)**](https://allenai.org/licenses/impact-mr) and completing all
  fields below.
extra_gated_fields:
  Your full name: text
  Organization or entity you are affiliated with: text
  State or country you are located in: text
  Contact email: text
  Please describe your intended use of the medium risk artifact(s): text
  I UNDERSTAND that the dataset is intended for research purposes and not for real-world use-cases: checkbox
  I AGREE to the terms and conditions of the MR Agreement above: checkbox
  I AGREE to AI2’s use of my information for legal notices and administrative matters: checkbox
  I CERTIFY that the information I have provided is true and accurate: checkbox
tags:
- not-for-all-audiences
size_categories:
- 100K<n<1M
---
# Dataset Card for ValuePrism

## Dataset Description

- **Paper:** https://arxiv.org/abs/2309.00779
- **Demo:** https://kaleido.allen.ai
- **Repository:** https://github.com/tsor13/kaleido
- **Datasheet for Datasets:** https://drive.google.com/file/d/1zDWvO0NljqxBMfDAGW7Jx60Iw54bjsEE/view?usp=sharing
- **License:** https://allenai.org/licenses/impact-mr
- **Point of Contact:** [Taylor Sorensen](mailto:tsor13@cs.washington.edu)

### Dataset Summary

ValuePrism was created 1) to understand what pluralistic human values, rights, and duties are already present in large language models, and 2) to serve as a resource to to support open, value pluralistic modeling (e.g., [Kaleido](https://huggingface.co/tsor13/kaleido-xl)). It contains human-written situations and machine-generated candidate values, rights, duties, along with their valences and post-hoc explanations relating them to the situations.
For additional documentation, see ValuePrism's [Datasheet](https://drive.google.com/file/d/1zDWvO0NljqxBMfDAGW7Jx60Iw54bjsEE/view?usp=sharing).

The dataset was created and intended for research purposes. It is openly released under AI2’s ImpACT license as a medium risk artifact.


### Supported Tasks

The dataset supports 4 tasks:
- **Generation (open-text)**
*What values, rights, and duties are relevant for a situation?*
Generate a value, right, or duty
that could be considered when reasoning about the action. Values are generated one at a time, as opposed to a batch.

- **Relevance (2-way classification)**
*Is a value relevant for a situation?* Some values are more relevant than others.

- **Valence (3-way classification)**
*Does the value support or oppose the action, or might it depend on context?*
Disentangling the valence is critical for understanding how plural considerations may interact with a decision.

- **Explanation (open-text)**
*How does the value relate to the action?* Generating a post-hoc rationale for why a value consideration may relate to a situation.


### Languages

All data is in English.

## Dataset Structure

### Dataset Splits
There are 6 data configurations:
- `full`: The full structured dataset of situations paired with values, rights, and duties paired with GPT-4. Only one split with all of the data.
- `generative`: Generative task train, val, and test splits.
- `relevance`: Relevance task train, val, and test splits.
- `valence`: Valence task train, val, and test splits.
- `explanation`: Explanation task train, val, and test splits.
- `mixture`: Generative, relevance, valence, and explanation tasks combined wtih train, val, and test splits.

### Data Fields

While different configurations have different fields, these are all the corresponding fields in the dataset:
- `situation` (string): A one sentence of a particular scenario or situation. For example, "buying some chocolate for my grandparents".
- `vrd` (string): Type of instance, either "Value", "Right", or "Duty".
- `text` (string): The text of the value, right, or duty. For example, "Honesty", "Right to property", "Duty to protect".
- `explanation` (string): A post-hoc explanation of why the specified value, right, or duty is relevant or important in the given situation. For example, "Buying chocolate for your grandparents can strengthen family connections and show appreciation for your relationship with them."
- `valence` (string): Indicates whether the value, right, or duty supports or opposes the action in the situation, or if it might depend on the context. Either "Supports", "Opposes", or "Either".
- `input` (string): For the seq2seq task (generative, relevance, valence, explanation), the input to the model.
- `output` (string): For the seq2seq task (generative, relevance, valence, explanation), the output of the model.


### Data Splits

All configurations (except for the raw outputs in `full`) have 80%/10%/10% train/validation/test splits.

## Dataset Creation

### Source Data

#### Data Collection

Situations are sourced from the Delphi user demo, and candidate values, rights, duties, their valences, and explanations connecting them to the situations are machine generated by GPT-4.

#### Who are the source language producers?

The situations are sourced from users of the Delphi user demo, for whom we do not have demographic information.

### Personal and Sensitive Information

There is no personal or sensitive information in ValuePrism.

## Considerations for Using the Data

### Social Impact of Dataset

We intend the dataset to be used to enable research and not to be used for real-world use or decision-making.

### Discussion of Biases

The value, right, and duty data was generated by GPT-4, which is known to exhibit [biases](https://arxiv.org/pdf/2304.03738.pdf). Thus, we expect ValuePrism to inherit biases from GPT-4.
That being said, we have tried to prompt the model to output a diversity of values in an attempt to mitigate bias with breadth.

## Additional Information

91% of values, rights, and duties were marked as high-quality by 3/3 annotators, and 87% of valence scores were marked as correct by 3/3 annotators.
Additionally, we perform a human study on the data and do not find large disparities in agreement between demographic groups tested, although future work in this area is a promising direction.
See [our paper] for more details and analysis.

### Licensing Information

ValuePrism is made available under the [**AI2
  ImpACT License - Medium Risk Artifacts (“MR
  Agreement”)**](https://allenai.org/licenses/impact-mr)

### Citation Information

Please cite [our paper](https://arxiv.org/abs/2309.00779) when using this dataset:
```
@misc{sorensen2023value,
      title={Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties}, 
      author={Taylor Sorensen and Liwei Jiang and Jena Hwang and Sydney Levine and Valentina Pyatkin and Peter West and Nouha Dziri and Ximing Lu and Kavel Rao and Chandra Bhagavatula and Maarten Sap and John Tasioulas and Yejin Choi},
      year={2023},
      eprint={2309.00779},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

#### Raw Dataset Statistics
The total, number of unique, and average number of generated values, rights, and duties per situation are shown.
| **Type**     | **Total** | **Unique** | **Per Situation** |
|--------------|-----------|------------|--------------------|
| **Situations**   | 31.0k     | 31.0k      | 1                  |
| **Values**       | 97.7k     | 4.2k       | 3.15               |
| **Rights**       | 49.0k     | 4.6k       | 1.58               |
| **Duties**       | 71.6k     | 12.8k      | 2.31               |



#### Task Dataset Statistics
|               | **Relevance** | **Valence** | **Generation** | **Explanation** | **Mixture** |
|---------------|------------|-------------|----------|-----------|-------------|
| **Train**     | 349k       | 175k        | 175k     | 175k      | 874k        |
| **Val**       | 44k        | 22k         | 22k      | 22k       | 109k        |
| **Test**      | 44k        | 22k         | 22k      | 22k       | 109k        |
| **Total**     | 437k       | 219k        | 219k     | 219k      | 1.1M        |