| --- |
| license: other |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| --- |
| |
| # CLEVRMath — `CLEVRMath_rejected` |
| |
| Rejection-sampled from the **CLEVRMath** train split. This split holds the **rejected** items — the `answer` field holds the official ground truth. |
| |
| | | | |
| |---|---| |
| | rows | 25,396 | |
| | QA pairs | 46,380 | |
| | shards | 167 | |
| | accepted / rejected (whole family) | 1,262,493 / 46,380 | |
| | accept rate | **96.5%** | |
| | verifier | `relaxed` | |
| |
| > The rejected split is training data, not just diagnostics: `answer` is the official ground truth, and `wrong_vlm` records what the model said instead. |
|
|
| ## How the data was produced |
|
|
| A VLM answers every question at temperature 0 with reasoning enabled. Its answer is compared with |
| the official ground truth by the verifier described below; matches go to `CLEVRMath_RS_think` and |
| `CLEVRMath_RS_nothink`, non-matches go to `CLEVRMath_rejected` with the ground truth preserved. |
|
|
| The `question` field is **byte-identical to the prompt the model actually received**, including |
| any appended instruction, so the stored training text and the inference-time input cannot drift |
| apart. |
|
|
| ## How a match is decided |
|
|
| ### `relaxed` — ChartQA relaxed accuracy |
|
|
| The official ChartQA metric. If both the prediction and the reference parse as numbers, the |
| answer is correct when it lands within **5% relative tolerance** of the reference — chart values |
| are read off axes, so exact digit agreement is not a fair requirement. Otherwise the two strings |
| must match exactly after lowercasing, trimming, and dropping a trailing period. |
| ## Second pass: model-as-judge |
|
|
| String verifiers cannot judge free-form answers at all, and they under-credit answers that are |
| correct but differently phrased. Every item the verifier rejected is therefore re-examined by the |
| same VLM in a text-only pass that sees the question, the reference and the candidate — no image, |
| no re-generation, it only grades text that was already produced. |
|
|
| The judge is instructed to compare against the reference and **never to solve the question |
| itself**, so an answer is not promoted merely because the model believes it is true. It answers |
| `CORRECT`, `WRONG`, or `UNSURE`; numbers must agree, and `UNSURE` is reserved for references that |
| are themselves unusable or ambiguous. **`UNSURE` is never counted as correct** — only a `CORRECT` verdict moves a row into the |
| accepted splits. `WRONG` and `UNSURE` both remain in the rejected split, so an unresolved case |
| is never silently promoted to a pass. |
|
|
| Calibration: run over items the string verifier had already accepted, the judge agreed on 59/60. |
| Spot-checking its overturns shows it discriminates rather than rubber-stamps — it keeps |
| `\frac{L}{n}` rejected against a reference of `\frac{L}{n-1}`, and returns `UNSURE` when the |
| model argues no finite answer exists against a numeric reference. |
|
|
| ## Schema |
|
|
| - `image` — HF `Image()` — renders directly in the dataset viewer |
| - `qa` — list of {`question`, `answer`, `wrong_vlm`, `reason`} |
| - `clean_meta` — per-row provenance and the cleaning policy that admitted it |
| - `image_sha256` — content hash of the image |
|
|
|
|
| ## Examples |
| Three rows taken straight from this split. Images are the **original files** as stored in the dataset, and long fields are shown in full. |
|
|
| ### Example 1 |
|
|
|  |
|
|
| | field | value | |
| |---|---| |
| | `question` | Question: Subtract all big blue things. Subtract all large rubber objects. How many objects are left?<br>Answer in short without any other content. | |
| | `answer` | 0 | |
| | `reason` | wrong_answer | |
| |
| <details> |
| <summary><code>wrong_vlm</code> (1 chars)</summary> |
|
|
| ``` |
| 2 |
| ``` |
|
|
| </details> |
|
|
|
|
| ### Example 2 |
|
|
|  |
|
|
| | field | value | |
| |---|---| |
| | `question` | Question: Add 1 matte objects. How many objects exist?<br>Answer in short without any other content. | |
| | `answer` | 10 | |
| | `reason` | wrong_answer | |
| |
| <details> |
| <summary><code>wrong_vlm</code> (1 chars)</summary> |
|
|
| ``` |
| 9 |
| ``` |
|
|
| </details> |
|
|
|
|
| ### Example 3 |
|
|
|  |
|
|
| | field | value | |
| |---|---| |
| | `question` | Subtract all gray objects. Subtract all gray rubber cylinders. How many objects are left?<br>Answer in short without any other content. | |
| | `answer` | 4 | |
| | `reason` | wrong_answer | |
| |
| <details> |
| <summary><code>wrong_vlm</code> (1 chars)</summary> |
|
|
| ``` |
| 5 |
| ``` |
|
|
| </details> |
|
|
|
|
| ## Cleaning applied before rejection sampling |
|
|
| - **Provenance check.** Each QA item records where it came from; items whose recorded source does |
| not resolve to the image they are attached to are dropped. |
| - **Task purity.** The pool's InfinityMM slice assigns samples to a family by image match rather |
| than by dataset origin, which pulls unrelated general-VQA questions into benchmark families. |
| Families where that content measured off-task keep only their genuine benchmark QA. |
| - **Multiple-choice normalisation.** Where options had been flattened into prose, they are |
| re-rendered as a lettered block and the reference is rewritten to the bare letter, so the task |
| is well posed and the answer is unambiguous. |
|
|
