{
  "name": "VStarBench",
  "release_date": "2026-05-15",
  "subsets": {
    "main": {
      "language": [
        "en"
      ],
      "modalities": [
        "single_image_start"
      ],
      "task_type": "multiple_choice_vqa",
      "prompt_template": "<image>{{ question }}",
      "mapping_from_source": {
        "media": {
          "from": "image",
          "type": "list",
          "min_items": 1,
          "max_items": 1
        },
        "id": {
          "from": "question_id"
        },
        "question": {
          "from": "text"
        },
        "answer": {
          "from": "label",
          "optional": true
        },
        "extra": {
          "category": {
            "from": "category"
          }
        },
        "source": {
          "format": "json",
          "url": {
            "test": "https://huggingface.co/datasets/craigwu/vstar_bench"
          }
        }
      },
      "prompt_template_source": {
        "origin": "source_column",
        "reference": "https://huggingface.co/datasets/craigwu/vstar_bench (source `text` column = full pre-rendered prompt: question + (A)/(B)/(C)/(D) options + 'Answer with the option's letter from the given choices directly.' trailer baked in)",
        "notes": "Tier 2.2: pass-through {{ question }} where question = upstream `text` column. All 191/191 rows carry pre-rendered prompt with options A-D inline and the answer-letter instruction. Cross-reference: lmms-eval vstar_bench task (https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/vstar_bench/utils.py) reformats the same data; mm-eval preserves the source column verbatim."
      }
    }
  }
}