

# Dataset Card for FELM

## Table of Contents
- [Dataset Card for FELM](#dataset-card-for-FELM)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Dataset Summary](#dataset-summary)
    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Data Instances](#data-instances)
    - [Data Fields](#data-fields)
  - [Dataset Creation](#dataset-creation)
    - [Source Data](#source-data)
      - [Initial Data Collection and Clean](#initial-data-collection-and-clean)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
  - [Additional Information](#additional-information)
    - [Licensing Information](#licensing-information)
    - [Citation Information](#citation-information)
    - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/hkust-nlp/felm
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary
[[Paper]](https://arxiv.org/abs/2310.00741) [[Github Repo]](https://github.com/hkust-nlp/felm)  
FELM is a meta-benchmark to evaluate factuality evaluation for large language models.<br>
The benchmark comprises 847 questions that span five distinct domains: world knowledge, science/technology, writing/recommendation, reasoning, and math. We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.<br>
We then obtain responses from ChatGPT for these prompts. For each response, we employ fine-grained annotation at the segment level, which includes reference links, identified error types, and the reasons behind these errors as provided by our annotators.
### Supported Tasks and Leaderboards

[Needs More Information]

### Languages

The text in the dataset is in English.

## Dataset Structure

### Data Instances
An example looks as follows:

```python
{"index": "0", 
 "source": "quora", 
 "prompt": "Which country or city has the maximum number of nuclear power plants?", 
 "response": "The United States has the highest number of nuclear power plants in the world, with 94 operating reactors. Other countries with a significant number of nuclear power plants include France, China, Russia, and South Korea.",
 "segmented_response": ["The United States has the highest number of nuclear power plants in the world, with 94 operating reactors.", "Other countries with a significant number of nuclear power plants include France, China, Russia, and South Korea."], 
 "labels": [false, true],
 "comment": ["As of December 2022, there were 92 operable nuclear power reactors in the United States.", ""], 
 "type": ["knowledge_error", null], 
 "ref": ["https://www.eia.gov/tools/faqs/faq.php?id=207&t=3"]}

```


### Data Fields


| Field Name  | Field Value | Description                                 |
| ----------- | ----------- | ------------------------------------------- |
| index         | Integer     | the order number of the data point          |
| source   | string      | the prompt source   |
| prompt           | string      | the prompt for generating response                   |
| response           | string      | the response of ChatGPT for prompt                  |
| segmented_response           | list      | segments of reponse                   |
| labels          | list      | factuality labels for segmented_response                  |
| comment      | list      | error reasons for segments with factual error  |
| type | list      |  error types for segments with factual error        |
| ref | list      |  reference links       |


## Dataset Creation


### Source Data

#### Initial Data Collection and Clean

We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.

The data is cleaned by authors.

### Annotations

#### Annotation process

We have developed an annotation tool and established annotation guidelines. All annotations undergo a double-check process, which involves review by both other annotators and an expert reviewer.
#### Who are the annotators?

The authors of the paper; Yuzhen Huang, Yikai Zhang, Tangjun Su.



## Additional Information

### Licensing Information

This dataset is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/)).

### Citation Information

```bibtex
@inproceedings{
chen2023felm,
title={FELM: Benchmarking Factuality Evaluation of Large Language Models},
author={Chen, Shiqi and Zhao, Yiran and Zhang, Jinghan and Chern, I-Chun and Gao, Siyang and Liu, Pengfei and He, Junxian},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={http://arxiv.org/abs/2310.00741}
}

```
### Contributions
[Needs More Information]

task_categories:text-generation language:en license:cc-by-nc-sa-4.0 arxiv:2310.00741 region:us
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation"], "pretty_name": "FELM"}
TAGS
#task_categories-text-generation #language-English #license-cc-by-nc-sa-4.0 #arxiv-2310.00741 #region-us 

Dataset Card for FELM
=====================


Table of Contents
-----------------


* Dataset Card for FELM
	+ Table of Contents
	+ Dataset Description
		- Dataset Summary
		- Supported Tasks and Leaderboards
		- Languages
	+ Dataset Structure
		- Data Instances
		- Data Fields
	+ Dataset Creation
		- Source Data
			* Initial Data Collection and Clean
			* Who are the source language producers?
		- Annotations
			* Annotation process
	+ Additional Information
		- Licensing Information
		- Citation Information
		- Contributions


Dataset Description
-------------------


* Homepage:
* Repository: URL
* Paper:
* Leaderboard:
* Point of Contact:


### Dataset Summary


[[Paper]](URL [[Github Repo]](URL  

FELM is a meta-benchmark to evaluate factuality evaluation for large language models.  

The benchmark comprises 847 questions that span five distinct domains: world knowledge, science/technology, writing/recommendation, reasoning, and math. We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.  

We then obtain responses from ChatGPT for these prompts. For each response, we employ fine-grained annotation at the segment level, which includes reference links, identified error types, and the reasons behind these errors as provided by our annotators.


### Supported Tasks and Leaderboards


### Languages


The text in the dataset is in English.


Dataset Structure
-----------------


### Data Instances


An example looks as follows:


### Data Fields


Field Name: index, Field Value: Integer, Description: the order number of the data point
Field Name: source, Field Value: string, Description: the prompt source
Field Name: prompt, Field Value: string, Description: the prompt for generating response
Field Name: response, Field Value: string, Description: the response of ChatGPT for prompt
Field Name: segmented\_response, Field Value: list, Description: segments of reponse
Field Name: labels, Field Value: list, Description: factuality labels for segmented\_response
Field Name: comment, Field Value: list, Description: error reasons for segments with factual error
Field Name: type, Field Value: list, Description: error types for segments with factual error
Field Name: ref, Field Value: list, Description: reference links


Dataset Creation
----------------


### Source Data


#### Initial Data Collection and Clean


We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.


The data is cleaned by authors.


### Annotations


#### Annotation process


We have developed an annotation tool and established annotation guidelines. All annotations undergo a double-check process, which involves review by both other annotators and an expert reviewer.


#### Who are the annotators?


The authors of the paper; Yuzhen Huang, Yikai Zhang, Tangjun Su.


Additional Information
----------------------


### Licensing Information


This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License).


### Contributions



### Dataset Summary


[[Paper]](URL [[Github Repo]](URL  

FELM is a meta-benchmark to evaluate factuality evaluation for large language models.  

The benchmark comprises 847 questions that span five distinct domains: world knowledge, science/technology, writing/recommendation, reasoning, and math. We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.  

We then obtain responses from ChatGPT for these prompts. For each response, we employ fine-grained annotation at the segment level, which includes reference links, identified error types, and the reasons behind these errors as provided by our annotators. ### Supported Tasks and Leaderboards ### Languages


The text in the dataset is in English.


Dataset Structure
----------------- ### Data Instances


An example looks as follows: ### Data Fields


Field Name: index, Field Value: Integer, Description: the order number of the data point
Field Name: source, Field Value: string, Description: the prompt source
Field Name: prompt, Field Value: string, Description: the prompt for generating response
Field Name: response, Field Value: string, Description: the response of ChatGPT for prompt
Field Name: segmented\_response, Field Value: list, Description: segments of reponse
Field Name: labels, Field Value: list, Description: factuality labels for segmented\_response
Field Name: comment, Field Value: list, Description: error reasons for segments with factual error
Field Name: type, Field Value: list, Description: error types for segments with factual error
Field Name: ref, Field Value: list, Description: reference links


Dataset Creation
---------------- ### Source Data #### Initial Data Collection and Clean


We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.


The data is cleaned by authors. ### Annotations #### Annotation process


We have developed an annotation tool and established annotation guidelines. All annotations undergo a double-check process, which involves review by both other annotators and an expert reviewer. #### Who are the annotators?


The authors of the paper; Yuzhen Huang, Yikai Zhang, Tangjun Su.


Additional Information
---------------------- ### Licensing Information


This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License). ### Contributions
TAGS
#task_categories-text-generation #language-English #license-cc-by-nc-sa-4.0 #arxiv-2310.00741 #region-us 
 ### Dataset Summary


[[Paper]](URL [[Github Repo]](URL  

FELM is a meta-benchmark to evaluate factuality evaluation for large language models.  

The benchmark comprises 847 questions that span five distinct domains: world knowledge, science/technology, writing/recommendation, reasoning, and math. We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.  

We then obtain responses from ChatGPT for these prompts. For each response, we employ fine-grained annotation at the segment level, which includes reference links, identified error types, and the reasons behind these errors as provided by our annotators. ### Supported Tasks and Leaderboards ### Languages


The text in the dataset is in English.


Dataset Structure
----------------- ### Data Instances


An example looks as follows: ### Data Fields


Field Name: index, Field Value: Integer, Description: the order number of the data point
Field Name: source, Field Value: string, Description: the prompt source
Field Name: prompt, Field Value: string, Description: the prompt for generating response
Field Name: response, Field Value: string, Description: the response of ChatGPT for prompt
Field Name: segmented\_response, Field Value: list, Description: segments of reponse
Field Name: labels, Field Value: list, Description: factuality labels for segmented\_response
Field Name: comment, Field Value: list, Description: error reasons for segments with factual error
Field Name: type, Field Value: list, Description: error types for segments with factual error
Field Name: ref, Field Value: list, Description: reference links


Dataset Creation
---------------- ### Source Data #### Initial Data Collection and Clean


We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.


The data is cleaned by authors. ### Annotations #### Annotation process


We have developed an annotation tool and established annotation guidelines. All annotations undergo a double-check process, which involves review by both other annotators and an expert reviewer. #### Who are the annotators?


The authors of the paper; Yuzhen Huang, Yikai Zhang, Tangjun Su.


Additional Information
---------------------- ### Licensing Information


This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License). ### Contributions
passage: TAGS
#task_categories-text-generation #language-English #license-cc-by-nc-sa-4.0 #arxiv-2310.00741 #region-us 
### Dataset Summary


[[Paper]](URL [[Github Repo]](URL  

FELM is a meta-benchmark to evaluate factuality evaluation for large language models.  

The benchmark comprises 847 questions that span five distinct domains: world knowledge, science/technology, writing/recommendation, reasoning, and math. We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.  

We then obtain responses from ChatGPT for these prompts. For each response, we employ fine-grained annotation at the segment level, which includes reference links, identified error types, and the reasons behind these errors as provided by our annotators.### Supported Tasks and Leaderboards### Languages


The text in the dataset is in English.


Dataset Structure
-----------------### Data Instances


An example looks as follows:### Data Fields


Field Name: index, Field Value: Integer, Description: the order number of the data point
Field Name: source, Field Value: string, Description: the prompt source
Field Name: prompt, Field Value: string, Description: the prompt for generating response
Field Name: response, Field Value: string, Description: the response of ChatGPT for prompt
Field Name: segmented\_response, Field Value: list, Description: segments of reponse
Field Name: labels, Field Value: list, Description: factuality labels for segmented\_response
Field Name: comment, Field Value: list, Description: error reasons for segments with factual error
Field Name: type, Field Value: list, Description: error types for segments with factual error
Field Name: ref, Field Value: list, Description: reference links


Dataset Creation
----------------### Source Data