Datasets:
File size: 2,227 Bytes
31f8b91 d51cea4 f0a3088 8b367d2 f0a3088 8b367d2 f0a3088 8b367d2 f0a3088 8b367d2 f0a3088 8b367d2 d6ea6c5 f0a3088 8b367d2 f0a3088 6510314 f0a3088 e54af0c a3c0b0f e54af0c b012459 e54af0c 2c4db54 e54af0c 2c4db54 e54af0c 238c102 e54af0c b012459 e54af0c b012459 e54af0c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
---
language:
- en
license: mit
dataset_info:
features:
- name: source
dtype: string
- name: author
dtype: string
- name: title
dtype: string
- name: description
dtype: string
- name: url
dtype: string
- name: urlToImage
dtype: string
- name: publishedAt
dtype: string
- name: content
dtype: string
- name: category_nist
dtype: string
- name: category
dtype: string
- name: id
dtype: string
- name: subreddit
dtype: string
- name: score
dtype: int64
- name: num_comments
dtype: int64
- name: created_time
dtype: timestamp[ns]
- name: top_comments
dtype: string
splits:
- name: train
num_bytes: 649243675
num_examples: 93259
download_size: 364163308
dataset_size: 649243675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# *REALM*: *RE*AL-World *A*pplication of Large *L*anguage *M*odels
## Dataset Description
- **Paper:** coming soon
- **Dashboard Demo:** https://realm-e7682.web.app/
- **License:** [mit]
- **Language(s) (NLP):** English
- **Point of Contact:** [Jingwen]([email protected])
### Dataset Summary
Large Language Models (LLMs), such as GPT-like models, have transformed industries and everyday life, creating significant societal impact. To better understand their real-world applications, we created the REALM Dataset, a collection of over 93k use cases sourced from Reddit posts and news articles, spanning 2020-06(when GPT was first released) to 2024-12. REALM focuses on two key aspects:
1. How LLMs are being used: Categorizing the wide range of applications, following [AI Use Taxonomy: A Human-Centered Approach](https://www.nist.gov/publications/ai-use-taxonomy-human-centered-approach).
2. Who is using them: Extracting the occupation attributes of current or potential end-users, categorized based on the [O*NET classification system](https://www.onetcenter.org/).
### Updates
**2025-2-15: Content Update.** Paper submitted to ACL 2025.
### Languages
English
### Data Fields
- `` (string):
### Citation Information
Please consider citing [our paper](\\) if you find this dataset useful:
```
@inproceedings{
\\\
}
```
|