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[
{
"title": "",
"header": [
{
"value": "Model",
"markdown": false,
"metadata": {}
},
{
"value": "EM",
"description": "Scenario introduced in this work to measure fine-grained legal reasoning through reverse entailment.\n\nQuasi-exact match: Fraction of instances that the predicted output matches a correct reference up to light processing.",
"markdown": false,
"lower_is_better": false,
"metadata": {
"metric": "EM",
"run_group": "LegalSupport"
}
},
{
"value": "Denoised inference time (s)",
"description": "Scenario introduced in this work to measure fine-grained legal reasoning through reverse entailment.\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": "LegalSupport"
}
},
{
"value": "# eval",
"description": "Scenario introduced in this work to measure fine-grained legal reasoning through reverse entailment.\n\n# eval: Number of evaluation instances.",
"markdown": false,
"metadata": {
"metric": "# eval",
"run_group": "LegalSupport"
}
},
{
"value": "# train",
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"markdown": false,
"metadata": {
"metric": "# train",
"run_group": "LegalSupport"
}
},
{
"value": "truncated",
"description": "Scenario introduced in this work to measure fine-grained legal reasoning through reverse entailment.\n\ntruncated: Fraction of instances where the prompt itself was truncated (implies that there were no in-context examples).",
"markdown": false,
"metadata": {
"metric": "truncated",
"run_group": "LegalSupport"
}
},
{
"value": "# prompt tokens",
"description": "Scenario introduced in this work to measure fine-grained legal reasoning through reverse entailment.\n\n# prompt tokens: Number of tokens in the prompt.",
"markdown": false,
"metadata": {
"metric": "# prompt tokens",
"run_group": "LegalSupport"
}
},
{
"value": "# output tokens",
"description": "Scenario introduced in this work to measure fine-grained legal reasoning through reverse entailment.\n\n# output tokens: Actual number of output tokens.",
"markdown": false,
"metadata": {
"metric": "# output tokens",
"run_group": "LegalSupport"
}
},
{
"value": "# trials",
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"markdown": false,
"metadata": {
"metric": "# trials",
"run_group": "LegalSupport"
}
}
],
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{
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{
"description": "1 matching runs, but no matching metrics",
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},
{
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"links": [
{
"text": "LaTeX",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/legal_support_legal_support_.tex"
},
{
"text": "JSON",
"href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/legal_support_legal_support_.json"
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