lite_pythia-1.4b-step2000/groups.json

[
  {
    "title": "All scenarios",
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        "metadata": {}
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      {
        "value": "Description",
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      {
        "value": "Adaptation method",
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        "metadata": {}
      },
      {
        "value": "# instances",
        "description": "Number of instances evaluated on",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# references",
        "description": "Number of references provided per instance",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# prompt tokens",
        "description": "Total number of prompt tokens",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# completion tokens",
        "description": "Total number of completion tokens",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# models",
        "description": "Number of models we're evaluating",
        "markdown": false,
        "metadata": {}
      }
    ],
    "rows": [
      [
        {
          "value": "Core scenarios",
          "href": "?group=core_scenarios",
          "markdown": false
        },
        {
          "value": "The scenarios where we evaluate all the models.",
          "markdown": true
        },
        {
          "value": "generation, multiple_choice_joint",
          "markdown": false
        },
        {
          "value": 242.0,
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          "markdown": false
        },
        {
          "value": 2.9411764705882355,
          "description": "min=1, mean=2.941, max=4, sum=150 (51)",
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        {
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        {
          "value": 522.4981052660844,
          "description": "min=1, mean=10.245, max=145.623, sum=522.498 (51)",
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          "value": 1,
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      ]
    ],
    "links": []
  },
  {
    "title": "Scenarios",
    "header": [
      {
        "value": "Group",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "Description",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "Adaptation method",
        "description": "Adaptation strategy (e.g., generation)",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# instances",
        "description": "Number of instances evaluated on",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# references",
        "description": "Number of references provided per instance",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# prompt tokens",
        "description": "Total number of prompt tokens",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# completion tokens",
        "description": "Total number of completion tokens",
        "markdown": false,
        "metadata": {}
      },
      {
        "value": "# models",
        "description": "Number of models we're evaluating",
        "markdown": false,
        "metadata": {}
      }
    ],
    "rows": [
      [
        {
          "value": "NarrativeQA",
          "href": "?group=narrative_qa",
          "markdown": false
        },
        {
          "value": "The NarrativeQA benchmark for reading comprehension over narratives [(Ko\u010disk\u00fd et al., 2017)](https://aclanthology.org/Q18-1023/).",
          "markdown": true
        },
        {
          "value": "",
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "value": 0,
          "markdown": false
        }
      ],
      [
        {
          "value": "NaturalQuestions (closed-book)",
          "href": "?group=natural_qa_closedbook",
          "markdown": false
        },
        {
          "value": "The NaturalQuestions [(Kwiatkowski et al., 2019)](https://aclanthology.org/Q19-1026/) benchmark for question answering based on naturally-occurring queries through Google Search. The input does not include the Wikipedia page with the answer.",
          "markdown": true
        },
        {
          "value": "",
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "value": 0,
          "markdown": false
        }
      ],
      [
        {
          "value": "NaturalQuestions (open-book)",
          "href": "?group=natural_qa_openbook_longans",
          "markdown": false
        },
        {
          "value": "The NaturalQuestions [(Kwiatkowski et al., 2019)](https://aclanthology.org/Q19-1026/) benchmark for question answering based on naturally-occurring queries through Google Search. The input includes the Wikipedia page with the answer.",
          "markdown": true
        },
        {
          "value": "",
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "markdown": false
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        {
          "markdown": false
        },
        {
          "markdown": false
        },
        {
          "value": 0,
          "markdown": false
        }
      ],
      [
        {
          "value": "OpenbookQA",
          "href": "?group=openbookqa",
          "markdown": false
        },
        {
          "value": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).",
          "markdown": true
        },
        {
          "value": "multiple_choice_joint",
          "markdown": false
        },
        {
          "value": 500.0,
          "description": "min=500, mean=500, max=500, sum=500 (1)",
          "markdown": false
        },
        {
          "value": 4.0,
          "description": "min=4, mean=4, max=4, sum=12 (3)",
          "markdown": false
        },
        {
          "value": 754.668,
          "description": "min=251.556, mean=251.556, max=251.556, sum=754.668 (3)",
          "markdown": false
        },
        {
          "value": 3.0,
          "description": "min=1, mean=1, max=1, sum=3 (3)",
          "markdown": false
        },
        {
          "value": 1,
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        }
      ],
      [
        {
          "value": "MMLU (Massive Multitask Language Understanding)",
          "href": "?group=mmlu",
          "markdown": false
        },
        {
          "value": "The Massive Multitask Language Understanding (MMLU) benchmark for knowledge-intensive question answering across 57 domains [(Hendrycks et al., 2021)](https://arxiv.org/pdf/2009.03300.pdf).",
          "markdown": true
        },
        {
          "value": "multiple_choice_joint",
          "markdown": false
        },
        {
          "value": 56.7,
          "description": "min=8, mean=56.7, max=114, sum=567 (10)",
          "markdown": false
        },
        {
          "value": 4.0,
          "description": "min=4, mean=4, max=4, sum=120 (30)",
          "markdown": false
        },
        {
          "value": 14170.841555023922,
          "description": "min=354.455, mean=472.361, max=612.798, sum=14170.842 (30)",
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        {
          "value": 30.0,
          "description": "min=1, mean=1, max=1, sum=30 (30)",
          "markdown": false
        },
        {
          "value": 1,
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      ],
      [
        {
          "value": "GSM8K (Grade School Math)",
          "href": "?group=gsm",
          "markdown": false
        },
        {
          "value": "The grade school math word problems dataset (GSM8K) for testing mathematical reasoning on grade-school math problems [(Cobbe et al., 2021)](https://arxiv.org/pdf/2110.14168.pdf).",
          "markdown": true
        },
        {
          "value": "generation",
          "markdown": false
        },
        {
          "value": 1000.0,
          "description": "min=1000, mean=1000, max=1000, sum=1000 (1)",
          "markdown": false
        },
        {
          "value": 1.0,
          "description": "min=1, mean=1, max=1, sum=3 (3)",
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        },
        {
          "value": 2818.746,
          "description": "min=939.582, mean=939.582, max=939.582, sum=2818.746 (3)",
          "markdown": false
        },
        {
          "value": 436.86899999999997,
          "description": "min=145.623, mean=145.623, max=145.623, sum=436.869 (3)",
          "markdown": false
        },
        {
          "value": 1,
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        }
      ],
      [
        {
          "value": "MATH",
          "href": "?group=math_chain_of_thought",
          "markdown": false
        },
        {
          "value": "The MATH benchmark for measuring mathematical problem solving on competition math problems with chain-of-thought style reasoning [(Hendrycks et al., 2021)](https://arxiv.org/pdf/2103.03874.pdf).",
          "markdown": true
        },
        {
          "value": "",
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        {
          "markdown": false
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        {
          "markdown": false
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        {
          "markdown": false
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        {
          "markdown": false
        },
        {
          "value": 0,
          "markdown": false
        }
      ],
      [
        {
          "value": "LegalBench",
          "href": "?group=legalbench",
          "markdown": false
        },
        {
          "value": "LegalBench is a large collaboratively constructed benchmark of legal reasoning tasks [(Guha et al, 2023)](https://arxiv.org/pdf/2308.11462.pdf).",
          "markdown": true
        },
        {
          "value": "generation",
          "markdown": false
        },
        {
          "value": 409.4,
          "description": "min=95, mean=409.4, max=1000, sum=2047 (5)",
          "markdown": false
        },
        {
          "value": 1.0,
          "description": "min=1, mean=1, max=1, sum=15 (15)",
          "markdown": false
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          "description": "min=1, mean=3.509, max=13.412, sum=52.629 (15)",
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        }
      ],
      [
        {
          "value": "MedQA",
          "href": "?group=med_qa",
          "markdown": false
        },
        {
          "value": "MedQA is an open domain question answering dataset composed of questions from professional medical board exams ([Jin et al. 2020](https://arxiv.org/pdf/2009.13081.pdf)).",
          "markdown": true
        },
        {
          "value": "",
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        {
          "markdown": false
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        {
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        {
          "value": 0,
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      ],
      [
        {
          "value": "WMT 2014",
          "href": "?group=wmt_14",
          "markdown": false
        },
        {
          "value": "WMT 2014 is a collection of machine translation datasets [(website)](https://www.statmt.org/wmt14/index.html).",
          "markdown": true
        },
        {
          "value": "",
          "markdown": false
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        {
          "markdown": false
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          "markdown": false
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        {
          "value": 0,
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]