{
  "name": "MEGA-Bench",
  "release_date": "2024-10-18",
  "subsets": {
    "main": {
      "language": [
        "en"
      ],
      "modalities": [
        "multi_image_interleave",
        "text"
      ],
      "task_type": "short_answer_qa",
      "score_pipeline": {
        "unsupported": "per-task-metric",
        "reason": "requires the benchmark's own evaluator (per-row metric dispatch)"
      },
      "score_params": {
        "metric_field": "task_name"
      },
      "score_protocol": {
        "reference": "lmms-eval@lmms_eval/tasks/megabench/metrics/metric_type.py:1-120 + metrics/scoring/* (45 per-task metric types: exact/near str match, set/dict equality+Jaccard, BLEU/GLEU, Levenshtein similarity, nbbox IoU, number_rel_diff_ratio, LaTeX equality, program_judge, symbolic planning, VLM-as-judge, ...) and tasks/megabench/README.md + evaluator.py — official protocol (adapted from TIGER-AI-Lab/MEGA-Bench) runs a stand-alone evaluator that dispatches each task to its own metric; core split = rule metrics, open split = GPT(-4o) judge; headline = macro mean over ~505 tasks.",
        "note": "The mm-eval copy merges test_core and test_open into one subset and stores only task_name in extra — the per-task metric/aggregation/parsing config required by the official evaluator is not shipped, so no executable score_pipeline can reproduce the official protocol (declared unsupported). Many metrics are fractional per-sample; official headline is macro-averaged per task, not per-sample mean."
      },
      "prompt_template": "{{ question }}",
      "mapping_from_source": {
        "media": {
          "from": "images",
          "type": "list",
          "min_items": 0,
          "max_items": 21
        },
        "id": {
          "from": "id"
        },
        "question": {
          "from": "question"
        },
        "answer": {
          "from": "answer",
          "optional": true
        },
        "extra": {
          "task_name": {
            "from": "task_name"
          }
        },
        "source": {
          "format": "json",
          "url": {
            "test_core": "https://huggingface.co/datasets/TIGER-Lab/MEGA-Bench",
            "test_open": "https://huggingface.co/datasets/TIGER-Lab/MEGA-Bench"
          }
        }
      },
      "prompt_template_source": {
        "origin": "official",
        "reference": "https://github.com/TIGER-AI-Lab/MEGA-Bench/blob/main/megabench/loader.py (task_description+example_text+query_text concatenated into question column at conversion time; template is bare {{ question }})",
        "notes": "Tier 1: MEGA-Bench: per-task scaffolding (task_description, example_text, query_text) is concatenated into the question column at conversion time; template is intentionally bare to avoid double-scaffolding."
      }
    }
  }
}