Datasets:
license: other
task_categories:
- visual-question-answering
tags:
- benchability
- figure4
BenchAbility Figure 4 -- held-out eval
The 10% candidate-dev half of the same 884,143-row pool the two training mixtures are drawn from, balanced per capability. Split by image, so no picture here appears in either mixture, and both arms are equally blind to it.
| capability | n |
|---|---|
| chart_reading / chart_reasoning | 250 / 250 |
| table_lookup / table_reasoning | 250 / 250 |
| document_qa / document_text_reading | 250 / 250 |
| diagram_and_infographic_understanding | 250 |
| scene_text_recognition | 250 |
| key_information_extraction | 168 (all that exists) |
Balanced rather than proportional: the pool is 38% chart_reasoning, so a proportional dev set would
measure that leaf precisely and the scarce ones not at all -- and the scarce ones are where the two
arms differ most. One row per image, because a second question on the same picture is not an
independent measurement.
This is an in-domain dev set, not the paper's evaluation. Same 20 sources, held out by image. It answers "did training move this capability at all", which separates a broken run from a real one. It cannot separate "learned the capability" from "learned these 20 datasets" -- that needs the unseen-source set (ChartQAPro, OCRBench v2, DUDE, MME-RealWorld) which is not in this pool.
Score with evaluate.py from the training bundle; measure the base model first, then each
checkpoint, and report gain = checkpoint - base per capability.