---
license: cc-by-4.0
tags:
  - vision-language
  - llm-as-judge
  - evaluation
  - causal-inference
  - multimodal
---

# GroundJudge: constructed artifacts and judge verdicts

Supporting data for **"Do Vision-Language Judges Use the Image? A Causal
Audit of Visual Grounding in Multimodal Evaluation"** (ICLR 2027
submission). Code: see the paper's GitHub repository.

This repo contains **only this project's own constructed/generated
artifacts** — every image variant, injected trace, and per-instance judge
verdict behind the paper's tables. It deliberately does **not** include:

- Raw source datasets (GQA, ChartQA, MathVista, ScienceQA, HallusionBench,
  VL-RewardBench) — these are public; download them from their original
  sources (see the paper's Appendix C and the `groundjudge/src/data/`
  ingestion scripts in the code repo for exact URLs/HF dataset IDs).
- Judge model weights (Qwen2.5-VL, InternVL3, LLaVA-OneVision,
  Llama-3.2-Vision, VisualPRM, SDXL-inpainting, SAM2) — these are public
  checkpoints on the Hugging Face Hub; load them directly via
  `transformers`/`diffusers` as the code does.

## Contents

```
images/<dataset>/<instance_id>/
    x.png            # original image
    x_plus.png       # content-preserving re-encoding (control)
    x_blank.png      # blanked image (ablation)
    x_mismatch.png   # mismatched image from a different instance (ablation)
    x_cf.png         # counterfactual edit (flips the ground truth)
    x_dagger.png     # artifact-matched null edit (same operator, no truth change)
    cf_meta.json     # per-instance edit metadata (region, method, target value/state)

verdicts/
    e0/{oracle,text_only}/<dataset>/verdicts_<split>.jsonl   # E0 positive-control verdicts
    pilot/<judge_id>/<dataset>/verdicts_<split>.jsonl        # E1 main-audit verdicts (every judge x image condition x trace condition)
    e2_extended/<judge_id>/<dataset>/verdicts_<split>.jsonl  # independent E2 candidate-pool verdicts (ChartQA, ScienceQA only)

injected/<dataset>/injected_<split>.jsonl   # clean/visual-error/logical-error trace text per instance
traces/<dataset>/traces_<split>.jsonl       # clean trace text per instance (pre-injection)
data_processed/<dataset>/instances_<split>.jsonl   # ingested ground-truth instances (the common schema every downstream stage reads)
```

## Datasets included

| Dataset | Split | n (in paper) | Status |
|---|---|---|---|
| ChartQA | val | 100 | in paper |
| HallusionBench | val | 104 | in paper |
| MathVista | testmini | 16 | in paper (FigureQA-sourced bar-comparison subset) |
| ScienceQA | test | 22 | in paper (State-capitals subset) |
| GQA | val | -- | **excluded negative result** — construction plateaued at 70% human-validated edit-validity, below the 95% pre-registered bar. Images/edits released for inspection per the paper's reproducibility statement, but GQA does not appear in any reported table. |

## Judges covered in `verdicts/`

`llava_onevision_7b`, `qwen25vl_7b`, `internvl3_8b`, `llama32_vision_11b`,
`gpt4o_class`, `gemini_class`, `claude_class`, `visualprm_8b` (plus
`_text_only` variants where applicable), and the `oracle`/`text_only`
E0 reference judges.

## Reproducing a number from this data

Each `verdicts/pilot/<judge_id>/<dataset>/verdicts_<split>.jsonl` file has
one row per `(instance_id, condition, trace_condition)` with the judge's
`verdict`/`score`. Feed it through `groundjudge/src/metrics_glue.py`'s
`run_audit()` (in the code repo) to reproduce the exact VGS/GEDR/LEDR/CI
numbers in Table 1 — this is the same function that produced them originally.