| --- |
| dataset_info: |
| features: |
| - name: url |
| dtype: string |
| - name: permalink |
| dtype: string |
| - name: comments |
| sequence: string |
| - name: num_comments |
| dtype: int64 |
| - name: subreddit |
| dtype: string |
| - name: title |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 4997779774 |
| num_examples: 590721 |
| download_size: 3184699498 |
| dataset_size: 4997779774 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: mit |
| --- |
| # BLIFT: Behavior-LLaVA Instruction Fine-Tuning Dataset |
|
|
|
|
| Paper: [**Teaching Human Behavior Improves Content Understanding Abilities of VLMs**](https://openreview.net/forum?id=TrKq4Wlwcz) |
|
|
| Website: [https://behavior-in-the-wild.github.io/behavior-llava.html](https://behavior-in-the-wild.github.io/behavior-llava.html) |
|
|
| --- |
|
|
| ## Dataset Summary |
|
|
| **BLIFT** (Behavior-LLaVA Instruction Fine-Tuning) is a large-scale multimodal instruction tuning dataset designed to teach **Vision-Language Models (VLMs)** human behavior. It contains over **730k images and videos** collected from Reddit and YouTube, annotated with **reciever behavior** such as **comments, likes, views, and replay graphs**. |
|
|
| By modeling these downstream receiver behaviors, training on BLIFT improves **content understanding** of VLMs, showing significant improvements across 46 tasks in image, video, text, and audio understanding. |
|
|
| <img src="./bllava-fig_2.png" alt="bllava-fig" width="1000"/> |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| Each sample in BLIFT includes: |
|
|
| | Field | Type | Description | |
| |------------------|-----------|-----------------------------------------------------------------------------| |
| | `permalink` | `string` | URL to the reddit post | |
| | `url` | `string` | Media URL | |
| | `title` | `string` | Title of the post or video | |
| | `comments` | `list[str]` | Top user comments (cleaned and filtered) | |
| | `num_comments` | `int` | Number of comments on the post | |
| | `subreddit` | `string` | Subreddit source | |
|
|
|
|
|
|
| --- |
|
|
| ## Data Sources |
|
|
| BLIFT combines high-quality behavioral data from two sources: |
|
|
| ### Reddit |
| - Subreddits: `r/pics`, `r/videos` |
| - Collected: 400k images, 330k videos |
| - Metadata: Upvotes and top comments |
| - Filtering: NSFW, bots, duplicates, minimum comment quality |
|
|
| ### YouTube |
| - 250k videos from ~6,000 verified channels via Wikidata |
| - Metadata: Likes, views, top comments, replay graphs |
| - Filtering: English language, minimum 10k views, NSFW, duplicates |
|
|
| <img src="./filtering-final.png" alt="filtering" width="1000"/> |
|
|
| --- |
|
|
|
|
|
|
| ## Benchmarks & Results |
|
|
| Using BLIFT to train **Behavior-LLaVA** (a fine-tuned LLaMA-Vid), the model outperforms base LLaMA-Vid and other supervised baselines on: |
|
|
| - 46 tasks |
| - 26 benchmark datasets |
| - Across image, video, audio, and text modalities |
|
|
| <img src="./radar_chart (1).png" alt="results" width="1000"/> |
|
|
|
|
| --- |
|
|
|
|
| ## 🔗 Citation |
|
|
| If you use BLIFT, please cite: |
|
|
| ```bibtex |
| @article{singh2024teaching, |
| title={Teaching Human Behavior Improves Content Understanding Abilities Of LLMs}, |
| author={Singh, Somesh and SI, Harini and Singla, Yaman K and Baths, Veeky and Shah, Rajiv Ratn and Chen, Changyou and Krishnamurthy, Balaji}, |
| journal={arXiv preprint arXiv:2405.00942}, |
| year={2024} |
| } |
| ``` |
|
|
| --- |
|
|
| ## Contact |
|
|
| Contact behavior-in-the-wild@googlegroups.com for questions and suggestions. |
