| | --- |
| | 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: |
| | <div style="box-shadow: 10px 10px 15px rgba(0, 0, 0, 0)"> |
| | <pre> |
| | @misc{cheng2025realm, |
| | title={REALM: A Dataset of Real-World LLM Use Cases}, |
| | author={Jingwen Cheng and Kshitish Ghate and Wenyue Hua and William Yang Wang and Hong Shen and Fei Fang}, |
| | year={2025}, |
| | archivePrefix={arXiv}, |
| | url={https://arxiv.org/abs/2503.18792} |
| | } |
| | </pre> |
| | </div> |
| |
|
