metadata

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

Website: 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.

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:

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

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

🔗 Citation

If you use BLIFT, please cite:

@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.