Datasets:
uid stringlengths 14 28 | source stringclasses 20
values | capability stringclasses 9
values | arm stringclasses 1
value | split_role stringclasses 1
value | question stringlengths 17 5.18k | answer stringlengths 1 1.53k | image dict |
|---|---|---|---|---|---|---|---|
figureqa-train-3419-10 | figureqa | chart_reasoning | eval | intervention | Is Violet Red the high median?
Answer yes or no. | No. | {
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figureqa-train-15686-3 | figureqa | chart_reasoning | eval | intervention | Is Dark Seafoam the maximum?
Answer yes or no. | No. | {
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figureqa-train-12282-2 | figureqa | chart_reasoning | eval | intervention | Is Turquoise the minimum?
Answer yes or no. | No. | {
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dvqa-train-14998-12 | dvqa | chart_reasoning | eval | intervention | Does the chart contain any negative values?
Provide a succinct answer. | No. | {
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plotqa-train-1039-48 | plotqa | chart_reasoning | eval | intervention | What is the difference between the Under 5 mortality rate (per 1,000 live births) of Under-5(female) in 2000 and that in 2015?
Give a very brief answer. | 48.6. | {
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dvqa-train-345-0 | dvqa | chart_reasoning | eval | intervention | How many objects are preferred by more than 6 people in at least one category?
Offer a terse response. | Three. | {
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plotqa-train-630-82 | plotqa | chart_reasoning | eval | intervention | What is the difference between the Amount exported (in US$) of Goods in 2009 and the Amount exported (in US$) of Services in 2013?
Your answer should be compact. | -2.48e+09. | {
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figureqa-train-15490-6 | figureqa | chart_reasoning | eval | intervention | Is Dark Orchid the low median?
Answer yes or no. | Yes. | {"bytes":"iVBORw0KGgoAAAANSUhEUgAAAj4AAAGQCAYAAACuzGqvAAAkk0lEQVR4nO3dCXTU1Rn38ScJARL2CMSENRHZkoACTX(...TRUNCATED) |
figureqa-train-15279-4 | figureqa | chart_reasoning | eval | intervention | Is Rosy Brown greater than Sky Blue?
Answer yes or no. | Yes. | {"bytes":"iVBORw0KGgoAAAANSUhEUgAAAnQAAAGQCAYAAAAnVP3GAAAuTUlEQVR4nO3dB3hUZfr38TshCSWAEiBA6AjSOyI2BA(...TRUNCATED) |
plotqa-train-1715-15 | plotqa | chart_reasoning | eval | intervention | "What is the difference between the highest and the lowest cost of damage caused due to the depletio(...TRUNCATED) | 1.24e+07. | {"bytes":"iVBORw0KGgoAAAANSUhEUgAABUEAAAK8CAYAAADBM37pAAB5hElEQVR4nO3dB5icVdk/4Gc3PQECgRCMdIgJoBQpCt(...TRUNCATED) |
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.
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