| --- |
| 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. |
|
|
