{
  "name": "ScreenQA",
  "release_date": "2026-05-25",
  "subsets": {
    "main": {
      "language": [
        "en"
      ],
      "modalities": [
        "single_image_start"
      ],
      "task_type": "short_answer_qa",
      "score_pipeline": [
        "exact-match",
        "rule-match"
      ],
      "score_params": {
        "string_match": "exact"
      },
      "score_protocol": {
        "reference": "Official google-research-datasets/screen_qa README + code/metrics.py (SQA-Short task): Exact Match and SQuAD-style token F1 against multiple ground-truth answers with SQuAD preprocessing, averaged over samples (also ScreenQA paper arXiv:2209.08199 §metrics). Neither lmms-eval nor VLMEvalKit implements ScreenQA (re-confirmed 2026-07-07: no screenqa task dir in either local clone; only screenspot* grounding tasks exist in lmms-eval).",
        "note": "Official headline reports BOTH EM and SQuAD F1; per-sample binary exact matching reproduces EM only — the fractional token-F1 aggregate is not reproduced. Official EM applies SQuAD normalization (lowercase, strip articles/punctuation), which is looser than the scorer's default exact matcher. Published copy is the third-party mirror rootsautomation/RICO-ScreenQA-Short (not the official google-research-datasets release); multi-reference answer lists are preserved. 597/8427 test rows (7.1%) have the single reference '<no answer>'."
      },
      "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": "screen_id"
        },
        "question": {
          "from": "question"
        },
        "answer": {
          "from": "ground_truth",
          "optional": true
        },
        "source": {
          "format": "huggingface",
          "url": {
            "train": "https://huggingface.co/datasets/rootsautomation/RICO-ScreenQA-Short",
            "validation": "https://huggingface.co/datasets/rootsautomation/RICO-ScreenQA-Short",
            "test": "https://huggingface.co/datasets/rootsautomation/RICO-ScreenQA-Short"
          }
        }
      },
      "prompt_template_source": {
        "origin": "fallback",
        "reference": "T1",
        "notes": "Tier 5: no higher tier publishes a prompt. T1: github.com/google-research-datasets/screen_qa publishes data + metrics-only code/metrics.py (grepped: no prompt strings); T2: no prompt column in rootsautomation/RICO-ScreenQA-Short; T3: no VLMEvalKit screenqa; T4: no lmms-eval tasks/screenqa (clone @047ec52; screenspot* tasks are a different benchmark). Template is the canonical single-image short-answer fallback."
      }
    }
  }
}