{
  "name": "VStarBench",
  "release_date": "2026-05-15",
  "subsets": {
    "main": {
      "language": [
        "en"
      ],
      "modalities": [
        "single_image_start"
      ],
      "task_type": "multiple_choice_qa",
      "score_pipeline": [
        "rule-match",
        "llm-match"
      ],
      "score_protocol": {
        "reference": "vlmevalkit@vlmeval/dataset/image_mcq.py:249-324 (ImageMCQDataset.evaluate_heuristic) -> vlmevalkit@vlmeval/dataset/utils/multiple_choice.py:475-508 mcq_vanilla_eval: can_infer rule prefetch, then GPT extractor (extract_answer_from_item @vlmeval/dataset/utils/multiple_choice.py:359-407) maps verbose output to a letter, rule-compare vs GT; VStarBench registered as plain ImageMCQDataset @vlmeval/dataset/image_mcq.py:127. Cross-check: lmms-eval@lmms_eval/tasks/vstar_bench/utils.py:45-108 grades with deterministic regex letter extraction (pure rule), per-category (direct_attributes / relative_position) + overall accuracy",
        "note": "Per-category breakdown (direct_attributes/relative_position, in extra.category) is aggregation-level only. Options are baked inline into the question text as '(A) ...' lines with the letter-answer directive; GT is a bare letter (verified via HF rows) — extraction must parse inline parenthesized options."
      },
      "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."
      }
    }
  }
}