# Dataset Card for WikiBio GPT-3 Hallucination Dataset

- GitHub repository: https://github.com/potsawee/selfcheckgpt
- Paper: [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896)

### Dataset Summary

- We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt: ```This is a Wikipedia passage about {concept}``` where `concept` represents an individual from the WikiBio dataset.
- We split the generated passages into sentences, and we annotate each sentence into one of the 3 options: (1) accurate (2) minor_inaccurate (3) major_inaccurate.
- We report the data statistics, annotation process, and inter-annotator agreement in our paper.
  
## Update
- v3 (5 May 2023): 238 test IDs have been annotated in total.
- v2 (6 April 2023): 142 test IDs have been annotated, GPT-3 sampled passages are now included in this dataset.
- v1 (15 March 2023): 65 test IDs -- here is `wiki_bio_test_idx` of the documents in v1 [[Link]](https://drive.google.com/file/d/1N3_ZQmr9yBbsOP2JCpgiea9oiNIu78Xw/view?usp=sharing)

## Dataset Structure

Each instance consists of:
- `gpt3_text`: GPT-3 generated passage
- `wiki_bio_text`: Actual Wikipedia passage (first paragraph)
- `gpt3_sentences`: `gpt3_text` split into sentences using `spacy`
- `annotation`: human annotation at the sentence level
- `wiki_bio_test_idx`: ID of the concept/individual from the original wikibio dataset (testset)
- `gpt3_text_samples`: list of 20 sampled passages (do_sample = True & temperature = 1.0)

### Citation Information
```
@misc{manakul2023selfcheckgpt,
      title={SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models}, 
      author={Potsawee Manakul and Adian Liusie and Mark J. F. Gales},
      year={2023},
      eprint={2303.08896},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
task_categories:text-classification size_categories:n<1K language:en license:cc-by-sa-3.0 arxiv:2303.08896 region:us
{"language": ["en"], "license": "cc-by-sa-3.0", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "gpt3_text", "dtype": "string"}, {"name": "wiki_bio_text", "dtype": "string"}, {"name": "gpt3_sentences", "sequence": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "wiki_bio_test_idx", "dtype": "int64"}, {"name": "gpt3_text_samples", "sequence": "string"}], "splits": [{"name": "evaluation", "num_bytes": 5042581, "num_examples": 238}], "download_size": 2561507, "dataset_size": 5042581}}
TAGS
#task_categories-text-classification #size_categories-n<1K #language-English #license-cc-by-sa-3.0 #arxiv-2303.08896 #region-us 

# Dataset Card for WikiBio GPT-3 Hallucination Dataset

- GitHub repository: URL
- Paper: SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

### Dataset Summary

- We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt:  where 'concept' represents an individual from the WikiBio dataset.
- We split the generated passages into sentences, and we annotate each sentence into one of the 3 options: (1) accurate (2) minor_inaccurate (3) major_inaccurate.
- We report the data statistics, annotation process, and inter-annotator agreement in our paper.
  
## Update
- v3 (5 May 2023): 238 test IDs have been annotated in total.
- v2 (6 April 2023): 142 test IDs have been annotated, GPT-3 sampled passages are now included in this dataset.
- v1 (15 March 2023): 65 test IDs -- here is 'wiki_bio_test_idx' of the documents in v1 [[Link]](URL

## Dataset Structure

Each instance consists of:
- 'gpt3_text': GPT-3 generated passage
- 'wiki_bio_text': Actual Wikipedia passage (first paragraph)
- 'gpt3_sentences': 'gpt3_text' split into sentences using 'spacy'
- 'annotation': human annotation at the sentence level
- 'wiki_bio_test_idx': ID of the concept/individual from the original wikibio dataset (testset)
- 'gpt3_text_samples': list of 20 sampled passages (do_sample = True & temperature = 1.0)



# Dataset Card for WikiBio GPT-3 Hallucination Dataset

- GitHub repository: URL
- Paper: SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models ### Dataset Summary

- We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt:  where 'concept' represents an individual from the WikiBio dataset.
- We split the generated passages into sentences, and we annotate each sentence into one of the 3 options: (1) accurate (2) minor_inaccurate (3) major_inaccurate.
- We report the data statistics, annotation process, and inter-annotator agreement in our paper. ## Update
- v3 (5 May 2023): 238 test IDs have been annotated in total.
- v2 (6 April 2023): 142 test IDs have been annotated, GPT-3 sampled passages are now included in this dataset.
- v1 (15 March 2023): 65 test IDs -- here is 'wiki_bio_test_idx' of the documents in v1 [[Link]](URL ## Dataset Structure

Each instance consists of:
- 'gpt3_text': GPT-3 generated passage
- 'wiki_bio_text': Actual Wikipedia passage (first paragraph)
- 'gpt3_sentences': 'gpt3_text' split into sentences using 'spacy'
- 'annotation': human annotation at the sentence level
- 'wiki_bio_test_idx': ID of the concept/individual from the original wikibio dataset (testset)
- 'gpt3_text_samples': list of 20 sampled passages (do_sample = True & temperature = 1.0)
TAGS
#task_categories-text-classification #size_categories-n<1K #language-English #license-cc-by-sa-3.0 #arxiv-2303.08896 #region-us 
 # Dataset Card for WikiBio GPT-3 Hallucination Dataset

- GitHub repository: URL
- Paper: SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models ### Dataset Summary

- We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt:  where 'concept' represents an individual from the WikiBio dataset.
- We split the generated passages into sentences, and we annotate each sentence into one of the 3 options: (1) accurate (2) minor_inaccurate (3) major_inaccurate.
- We report the data statistics, annotation process, and inter-annotator agreement in our paper. ## Update
- v3 (5 May 2023): 238 test IDs have been annotated in total.
- v2 (6 April 2023): 142 test IDs have been annotated, GPT-3 sampled passages are now included in this dataset.
- v1 (15 March 2023): 65 test IDs -- here is 'wiki_bio_test_idx' of the documents in v1 [[Link]](URL ## Dataset Structure

Each instance consists of:
- 'gpt3_text': GPT-3 generated passage
- 'wiki_bio_text': Actual Wikipedia passage (first paragraph)
- 'gpt3_sentences': 'gpt3_text' split into sentences using 'spacy'
- 'annotation': human annotation at the sentence level
- 'wiki_bio_test_idx': ID of the concept/individual from the original wikibio dataset (testset)
- 'gpt3_text_samples': list of 20 sampled passages (do_sample = True & temperature = 1.0)
passage: TAGS
#task_categories-text-classification #size_categories-n<1K #language-English #license-cc-by-sa-3.0 #arxiv-2303.08896 #region-us 
# Dataset Card for WikiBio GPT-3 Hallucination Dataset

- GitHub repository: URL
- Paper: SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models### Dataset Summary

- We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt:  where 'concept' represents an individual from the WikiBio dataset.
- We split the generated passages into sentences, and we annotate each sentence into one of the 3 options: (1) accurate (2) minor_inaccurate (3) major_inaccurate.
- We report the data statistics, annotation process, and inter-annotator agreement in our paper.## Update
- v3 (5 May 2023): 238 test IDs have been annotated in total.
- v2 (6 April 2023): 142 test IDs have been annotated, GPT-3 sampled passages are now included in this dataset.
- v1 (15 March 2023): 65 test IDs -- here is 'wiki_bio_test_idx' of the documents in v1 [[Link]](URL## Dataset Structure

Each instance consists of:
- 'gpt3_text': GPT-3 generated passage
- 'wiki_bio_text': Actual Wikipedia passage (first paragraph)
- 'gpt3_sentences': 'gpt3_text' split into sentences using 'spacy'
- 'annotation': human annotation at the sentence level
- 'wiki_bio_test_idx': ID of the concept/individual from the original wikibio dataset (testset)
- 'gpt3_text_samples': list of 20 sampled passages (do_sample = True & temperature = 1.0)