---
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](chengjw21@gmail.com)

### 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{
  \\\
}
```