| --- |
| license: cc-by-nc-4.0 |
| language: [en, zh] |
| task_categories: [visual-question-answering] |
| pretty_name: VisualPun-Bench |
| size_categories: [n<1K] |
| configs: |
| - config_name: default |
| data_files: data/train-*.parquet |
| --- |
| |
| # VisualPun-Bench |
|
|
| A bilingual visual-pun benchmark: 376 pun images (276 English, 100 Mandarin), each with one question, one |
| reference punchline, and the annotated chain of reasoning hops (1,589 hops) that leads from the image to the pun. |
| A model sees the image and the question and must produce the pun phrase the image encodes; the hop questions probe each |
| component skill separately. |
|
|
| | column | content | |
| |---|---| |
| | `id` | item id | |
| | `language` | `EN` or `CN` | |
| | `image` | the pun image (PIL image) | |
| | `question` | the question asked about the image | |
| | `answer` | the reference punchline | |
| | `hops` | list of annotated hops, in chain order: `hop` (index), `type` (object recognition, OCR, spatial, action recognition, context, punch linguistic), `question`, `answer`, `annotation` (the annotator's original note) | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("Icey444/VisualPun-Bench", split="train") |
| row = ds[0] |
| row["image"].size # PIL.Image |
| row["question"], row["answer"] |
| for h in row["hops"]: print(h["hop"], h["type"], h["question"], "->", h["answer"]) |
| ``` |
| Hop questions are meant to be asked independently of one another; each supplies in its wording the context it needs from |
| earlier hops, so errors do not propagate. Open-ended answers are graded against the reference as correct / partial / |
| incorrect by the LLM judge described in the paper; partial answers do not count as correct. |
|
|
| ## License |
|
|
| Data and annotations are released under CC BY-NC 4.0. Images are included for research use only. |
|
|
