README.md

---
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 [email protected] for questions and suggestions.