metadata: add score_type/score_params/score_protocol (official grading protocol taxonomy); task_type is now format-only (judge_scored migrated to vqa + llm_judge). See dataset-upload skill references/metadata-json.md.

metadata.json

@@ -10,6 +10,19 @@
         "single_image_start"
       ],
       "task_type": "vqa",
+      "score_type": "llm_judge",
+      "score_params": {
+        "rubric": "scale",
+        "scale": [
+          0,
+          6
+        ],
+        "official_judge_model": "gpt-4"
+      },
+      "score_protocol": {
+        "reference": "Official eval script https://huggingface.co/datasets/Shengcao1006/MMHal-Bench/blob/main/eval_gpt4.py — GPT-4 rates each response 0-6 (informativeness x hallucination rubric: 0-2 = hallucination present, 3-6 = none) given image_content object list, question, and standard human answer. Not implemented in VLMEvalKit or lmms-eval.",
+        "note": "Official headline = average rating (0-6) AND hallucination rate = fraction of responses rated 0-2, with per-question-type breakdown (8 types x 12 questions, 96 rows — matches published num_rows_total=96). Per-sample binary grading deviates. mm-eval copy lacks the `image_content` object list the official judge template requires; per-row `question_type` extra carries MMHal categories (attribute/relation/holistic/other...), NOT scorer vocabulary — scorer must not treat it as a question_type signal."
+      },
       "prompt_template": "<image>{{ question }}",
       "mapping_from_source": {
         "media": {