{
  "name": "BenchLMM",
  "release_date": "2024-07-01",
  "subsets": {
    "main": {
      "language": [
        "en"
      ],
      "modalities": [
        "single_image_start"
      ],
      "task_type": "short_answer_qa",
      "score_pipeline": [
        "llm-judge"
      ],
      "score_params": {
        "rubric": "scale",
        "scale": [
          0,
          1
        ],
        "official_judge_model": "gpt-4-0613"
      },
      "score_protocol": {
        "reference": "official@github.com/AIFEG/BenchLMM evaluate/gpt_evaluation_script.py — GPT judge prompt: 'Compare the ground truth and prediction from AI models, to give a correctness score for the prediction' ignoring case/grammar, '/' = multiple acceptable answers, similar meaning gets full marks; judge outputs a score in {0.0,0.1,...,1.0}; example model gpt-4-0613; final metric = mean score (avg_score.py).",
        "note": "Every sample is LLM-judged with fractional credit on a 0-1 scale (11 discrete values); headline is the mean per-sample score, not binary accuracy. The mm-eval copy has only 96 rows (test split) whereas official BenchLMM spans many style/domain files (CT, MRI, infrared, AD, RS, styles, Robots/Games, each with its own eval variant) — published copy appears to be a small subset."
      },
      "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": "id"
        },
        "question": {
          "from": "question"
        },
        "answer": {
          "from": "answer",
          "optional": true
        },
        "source": {
          "format": "json",
          "url": {
            "test": "https://huggingface.co/datasets/AIFEG/BenchLMM"
          }
        }
      },
      "prompt_template_source": {
        "origin": "official",
        "reference": "https://github.com/AIFEG/BenchLMM/blob/main/baseline/LLaVA/BenchGPT_LLaVA_model_vqa.py#L44-L49 (qs = line['text']; DEFAULT_IMAGE_TOKEN + '\\n' + qs); trailer '\\nAnswer the question using a single word or phrase.' is embedded in every official jsonl 'text' field, e.g. https://github.com/AIFEG/BenchLMM/blob/main/jsonl/Benchmark_style_cartoon.jsonl",
        "notes": "Tier 1: BenchLMM authors' own inference script feeds <image>\\n + question, and the official jsonl question text carries the '\\nAnswer the question using a single word or phrase.' trailer; the mm-eval template reproduces this (trailer re-appended because the mm-eval question column is trailer-free)."
      }
    }
  }
}