classic_pythia-2.8b-step2000/groups/json/knowledge_efficiency.json

{
  "title": "Efficiency",
  "header": [
    {
      "value": "Model",
      "markdown": false,
      "metadata": {}
    },
    {
      "value": "Mean win rate",
      "description": "How many models this model outperforms on average (over columns).",
      "markdown": false,
      "lower_is_better": false,
      "metadata": {}
    },
    {
      "value": "NaturalQuestions (closed-book) - Denoised inference time (s)",
      "description": "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.\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.",
      "markdown": false,
      "lower_is_better": true,
      "metadata": {
        "metric": "Denoised inference time (s)",
        "run_group": "NaturalQuestions (closed-book)"
      }
    },
    {
      "value": "HellaSwag - Denoised inference time (s)",
      "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.",
      "markdown": false,
      "lower_is_better": true,
      "metadata": {
        "metric": "Denoised inference time (s)",
        "run_group": "HellaSwag"
      }
    },
    {
      "value": "OpenbookQA - Denoised inference time (s)",
      "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.",
      "markdown": false,
      "lower_is_better": true,
      "metadata": {
        "metric": "Denoised inference time (s)",
        "run_group": "OpenbookQA"
      }
    },
    {
      "value": "TruthfulQA - Denoised inference time (s)",
      "description": "The TruthfulQA benchmarking for measuring model truthfulness and commonsense knowledge in question answering [(Lin et al., 2022)](https://aclanthology.org/2022.acl-long.229/).\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.",
      "markdown": false,
      "lower_is_better": true,
      "metadata": {
        "metric": "Denoised inference time (s)",
        "run_group": "TruthfulQA"
      }
    },
    {
      "value": "MMLU - Denoised inference time (s)",
      "description": "The Massive Multitask Language Understanding (MMLU) benchmark for knowledge-intensive question answering across 57 domains [(Hendrycks et al., 2021)](https://openreview.net/forum?id=d7KBjmI3GmQ).\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.",
      "markdown": false,
      "lower_is_better": true,
      "metadata": {
        "metric": "Denoised inference time (s)",
        "run_group": "MMLU"
      }
    },
    {
      "value": "WikiFact - Denoised inference time (s)",
      "description": "Scenario introduced in this work, inspired by [Petroni et al. (2019)](https://aclanthology.org/D19-1250/), to more extensively test factual knowledge.\n\nDenoised inference runtime (s): Average time to process a request to the model minus performance contention by using profiled runtimes from multiple trials of SyntheticEfficiencyScenario.",
      "markdown": false,
      "lower_is_better": true,
      "metadata": {
        "metric": "Denoised inference time (s)",
        "run_group": "WikiFact"
      }
    }
  ],
  "rows": [
    [
      {
        "value": "EleutherAI/pythia-2.8b",
        "description": "",
        "markdown": false
      },
      {
        "markdown": false
      },
      {
        "description": "No matching runs",
        "markdown": false
      },
      {
        "description": "No matching runs",
        "markdown": false
      },
      {
        "description": "No matching runs",
        "markdown": false
      },
      {
        "description": "No matching runs",
        "markdown": false
      },
      {
        "description": "5 matching runs, but no matching metrics",
        "markdown": false
      },
      {
        "description": "10 matching runs, but no matching metrics",
        "markdown": false
      }
    ]
  ],
  "links": [
    {
      "text": "LaTeX",
      "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/knowledge_efficiency.tex"
    },
    {
      "text": "JSON",
      "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/knowledge_efficiency.json"
    }
  ],
  "name": "efficiency"
}