---
license: cc-by-nc-4.0
task_categories:
  - audio-classification
  - video-text-to-text
  - question-answering
language:
  - en
size_categories:
  - 1K<n<10K
tags:
  - sft
  - instruction-tuning
  - audio-visual
  - multimodal
  - video-understanding
  - mllm
pretty_name: "Hans-SFT-4K: Supervised fine-tuning data for the audio-visual Clever Hans"
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.json
---

# Hans-SFT-4K · SFT recipe for the audio-visual Clever Hans

Supervised fine-tuning (SFT) data accompanying the paper
**[When Vision Speaks for Sound](https://arxiv.org/abs/2605.16403)**.

Like the [original Clever Hans](https://en.wikipedia.org/wiki/Clever_Hans) —
the horse that *looked* like he could do arithmetic but was actually reading
his trainer's body language — video-capable MLLMs often *look* like they
can hear: they answer audio questions by reading visual cues and never
verifying the audio stream.

**Hans-SFT-4K** is the **3,834-sample SFT mix** that teaches models to
honestly listen for audio temporal cues (Shift-style interventions),
producing the SFT checkpoint that the larger preference dataset
[Hans-10K](https://huggingface.co/datasets/Rakancorle1/hans-10k) further
aligns via DPO.

## What's in this repo

| File | Description | Size |
|---|---|---|
| `train.json` | 3,834 SFT records | ~3 MB |
| `sft_train_media.zip`  | All referenced videos + audios | ~7 GB |

## How to use

```bash
# 1. Download
hf download Rakancorle1/hans-sft-4k --repo-type=dataset --local-dir hans-sft

cd hans-sft

# 2. Unzip media — paths inside train.json resolve automatically
unzip -q sft_train_media.zip

# After unzipping, you'll have:
#   videos/original/<file>.mp4    (1,278 files)
#   videos/shifted/<file>.mp4     (2,556 files)
#   audios/original/<file>.wav
#   audios/shifted/<file>.wav
```

```python
from datasets import load_dataset

ds = load_dataset("Rakancorle1/hans-sft-4k")
```

## Composition

3,834 SFT records over 1,278 unique source clips, each clip seen in three
conditions (1 original + 2 audio-shifted versions: early-shifted +
delay-shifted). The chosen response describes the actual audio-visual
alignment honestly:

- **original** clips → "the audio is aligned with the visual event"
- **shifted** clips → "the audio is offset by ~X seconds, the visual moment
  does not line up"

## Schema

Each record in `train.json`:

```jsonc
{
  "id": "sft_000001",
  "messages": [
    { "role": "user",      "content": "<video><audio>...question..." },
    { "role": "assistant", "content": "...honest answer..." }
  ],
  "videos": ["videos/{original|shifted}/<file>.mp4"],
  "audios": ["audios/{original|shifted}/<file>.wav"]
}
```

## Relation to other releases

- **[Rakancorle1/hans-10k](https://huggingface.co/datasets/Rakancorle1/hans-10k)** — the 10,383-sample DPO preference data used *after* SFT to further cure the Clever Hans effect.
- The two datasets share the same source video corpus for the THUD
  intervention subset; you don't need to download both to access videos,
  but each dataset is self-contained for convenience.

## Citation

```bibtex
@article{wen2026whenvisionspeaksforsound,
  title     = {When Vision Speaks for Sound},
  author    = {Xiaofei Wen and Wenjie Jacky Mo and Xingyu Fu and Rui Cai and
               Tinghui Zhu and Wendi Li and Yanan Xie and Muhao Chen and Peng Qi},
  year      = {2026},
  url       = {https://arxiv.org/abs/2605.16403}
}
```

## License

Released under **CC-BY-NC-4.0** for the annotations. Underlying media
(UAG-Oops style clips) retains its respective upstream license — please
consult the original source datasets when using or redistributing.