{
  "name": "SLAKE",
  "release_date": "2026-05-25",
  "subsets": {
    "main": {
      "language": [
        "en"
      ],
      "modalities": [
        "single_image_start"
      ],
      "task_type": "short_answer_qa",
      "score_pipeline": [
        "exact-match",
        "rule-match",
        "llm-judge"
      ],
      "score_protocol": {
        "reference": "No dedicated lmms-eval or VLMEvalKit task on disk (image_shortqa.py grep for SLAKE = empty; confirmed absent). SLAKE official metric is med-VQA accuracy: closed-ended (yes/no) exact-match accuracy + open-ended answer accuracy. rows/SLAKE.json shows short closed and open answers ('MRI','Abdomen','T2'). Classified by analogy to VLMEvalKit ShortQA grader used for PathVQA (image_shortqa.py:87-142).",
        "note": "Official SLAKE = closed accuracy + open accuracy (exact/token match). Modeled as rule_llm_judge: exact match for closed yes/no, LLM judge for open medical answers. English subset only (mdwiratathya/SLAKE-vqa-english); official SLAKE is bilingual EN+ZH."
      },
      "prompt_template": "<image>{{ question }}\nAnswer the question using a single word or phrase.",
      "mapping_from_source": {
        "media": {
          "from": "image",
          "type": "list",
          "min_items": 1,
          "max_items": 1
        },
        "id": {
          "from": "__index__"
        },
        "question": {
          "from": "question"
        },
        "answer": {
          "from": "answer",
          "optional": true
        },
        "source": {
          "format": "huggingface",
          "url": {
            "train": "https://huggingface.co/datasets/mdwiratathya/SLAKE-vqa-english",
            "validation": "https://huggingface.co/datasets/mdwiratathya/SLAKE-vqa-english",
            "test": "https://huggingface.co/datasets/mdwiratathya/SLAKE-vqa-english"
          }
        }
      },
      "prompt_template_source": {
        "origin": "fallback",
        "reference": "T1",
        "notes": "Tier 5: no higher tier publishes a prompt. T1: SLAKE (2021) is classification-era (BAN/MEVF baselines), no generative prompt; LLaVA-Med (closest official-adjacent eval, model_vqa_med.py#L199-L223) feeds the raw question through its own conversation template — a harness convention, not a benchmark prompt spec. T2: no prompt column in mdwiratathya/SLAKE-vqa-english. T3: no VLMEvalKit slake dataset. T4: no lmms-eval tasks/slake (clone @047ec52). Template is the canonical single-image short-answer fallback."
      }
    }
  }
}