[ { "title": "Accuracy", "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) - F1", "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\nF1: Average F1 score in terms of word overlap between the model output and correct reference.", "markdown": false, "lower_is_better": false, "metadata": { "metric": "F1", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "HellaSwag - EM", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "HellaSwag" } }, { "value": "OpenbookQA - EM", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "OpenbookQA" } }, { "value": "TruthfulQA - EM", "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\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "TruthfulQA" } }, { "value": "MMLU - EM", "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\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "MMLU" } }, { "value": "WikiFact - EM", "description": "Scenario introduced in this work, inspired by [Petroni et al. (2019)](https://aclanthology.org/D19-1250/), to more extensively test factual knowledge.\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": "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 }, { "value": 0.27487719298245616, "description": "min=0.16, mean=0.275, max=0.42, sum=1.374 (5)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "mmlu:subject=abstract_algebra,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=college_chemistry,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=computer_security,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=econometrics,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=us_foreign_policy,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] }, { "value": 0.0075921845770110154, "description": "min=0, mean=0.008, max=0.034, sum=0.076 (10)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "wikifact:k=5,subject=author,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=currency,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=discoverer_or_inventor,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=instance_of,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=medical_condition_treated,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=part_of,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=place_of_birth,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=plaintiff,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=position_held,model=EleutherAI_pythia-2.8b", "wikifact:k=5,subject=symptoms_and_signs,model=EleutherAI_pythia-2.8b" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/knowledge_accuracy.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/knowledge_accuracy.json" } ], "name": "accuracy" }, { "title": "Calibration", "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) - ECE (10-bin)", "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\n10-bin expected calibration error: The average difference between the model's confidence and accuracy, averaged across 10 bins where each bin contains an equal number of points (only computed for classification tasks). Warning - not reliable for small datasets (e.g., with < 300 examples) because each bin will have very few examples.", "markdown": false, "lower_is_better": true, "metadata": { "metric": "ECE (10-bin)", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "HellaSwag - ECE (10-bin)", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\n10-bin expected calibration error: The average difference between the model's confidence and accuracy, averaged across 10 bins where each bin contains an equal number of points (only computed for classification tasks). Warning - not reliable for small datasets (e.g., with < 300 examples) because each bin will have very few examples.", "markdown": false, "lower_is_better": true, "metadata": { "metric": "ECE (10-bin)", "run_group": "HellaSwag" } }, { "value": "OpenbookQA - ECE (10-bin)", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\n10-bin expected calibration error: The average difference between the model's confidence and accuracy, averaged across 10 bins where each bin contains an equal number of points (only computed for classification tasks). Warning - not reliable for small datasets (e.g., with < 300 examples) because each bin will have very few examples.", "markdown": false, "lower_is_better": true, "metadata": { "metric": "ECE (10-bin)", "run_group": "OpenbookQA" } }, { "value": "TruthfulQA - ECE (10-bin)", "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\n10-bin expected calibration error: The average difference between the model's confidence and accuracy, averaged across 10 bins where each bin contains an equal number of points (only computed for classification tasks). Warning - not reliable for small datasets (e.g., with < 300 examples) because each bin will have very few examples.", "markdown": false, "lower_is_better": true, "metadata": { "metric": "ECE (10-bin)", "run_group": "TruthfulQA" } }, { "value": "MMLU - ECE (10-bin)", "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\n10-bin expected calibration error: The average difference between the model's confidence and accuracy, averaged across 10 bins where each bin contains an equal number of points (only computed for classification tasks). Warning - not reliable for small datasets (e.g., with < 300 examples) because each bin will have very few examples.", "markdown": false, "lower_is_better": true, "metadata": { "metric": "ECE (10-bin)", "run_group": "MMLU" } } ], "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 }, { "value": 0.1935229606887982, "description": "min=0.149, mean=0.194, max=0.247, sum=0.968 (5)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "mmlu:subject=abstract_algebra,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=college_chemistry,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=computer_security,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=econometrics,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=us_foreign_policy,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/knowledge_calibration.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/knowledge_calibration.json" } ], "name": "calibration" }, { "title": "Robustness", "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) - F1 (Robustness)", "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\nF1: Average F1 score in terms of word overlap between the model output and correct reference.\n- Perturbation Robustness: Computes worst case over different robustness perturbations (misspellings, formatting, contrast sets).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "F1", "run_group": "NaturalQuestions (closed-book)", "perturbation": "Robustness" } }, { "value": "HellaSwag - EM (Robustness)", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.\n- Perturbation Robustness: Computes worst case over different robustness perturbations (misspellings, formatting, contrast sets).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "HellaSwag", "perturbation": "Robustness" } }, { "value": "OpenbookQA - EM (Robustness)", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.\n- Perturbation Robustness: Computes worst case over different robustness perturbations (misspellings, formatting, contrast sets).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "OpenbookQA", "perturbation": "Robustness" } }, { "value": "TruthfulQA - EM (Robustness)", "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\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.\n- Perturbation Robustness: Computes worst case over different robustness perturbations (misspellings, formatting, contrast sets).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "TruthfulQA", "perturbation": "Robustness" } }, { "value": "MMLU - EM (Robustness)", "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\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.\n- Perturbation Robustness: Computes worst case over different robustness perturbations (misspellings, formatting, contrast sets).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "MMLU", "perturbation": "Robustness" } } ], "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 }, { "value": 0.24035087719298245, "description": "min=0.16, mean=0.24, max=0.36, sum=1.202 (5)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "mmlu:subject=abstract_algebra,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=college_chemistry,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=computer_security,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=econometrics,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=us_foreign_policy,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/knowledge_robustness.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/knowledge_robustness.json" } ], "name": "robustness" }, { "title": "Fairness", "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) - F1 (Fairness)", "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\nF1: Average F1 score in terms of word overlap between the model output and correct reference.\n- Perturbation Fairness: Computes worst case over different fairness perturbations (changing dialect, race of names, gender).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "F1", "run_group": "NaturalQuestions (closed-book)", "perturbation": "Fairness" } }, { "value": "HellaSwag - EM (Fairness)", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.\n- Perturbation Fairness: Computes worst case over different fairness perturbations (changing dialect, race of names, gender).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "HellaSwag", "perturbation": "Fairness" } }, { "value": "OpenbookQA - EM (Fairness)", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.\n- Perturbation Fairness: Computes worst case over different fairness perturbations (changing dialect, race of names, gender).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "OpenbookQA", "perturbation": "Fairness" } }, { "value": "TruthfulQA - EM (Fairness)", "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\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.\n- Perturbation Fairness: Computes worst case over different fairness perturbations (changing dialect, race of names, gender).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "TruthfulQA", "perturbation": "Fairness" } }, { "value": "MMLU - EM (Fairness)", "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\nExact match: Fraction of instances that the predicted output matches a correct reference exactly.\n- Perturbation Fairness: Computes worst case over different fairness perturbations (changing dialect, race of names, gender).", "markdown": false, "lower_is_better": false, "metadata": { "metric": "EM", "run_group": "MMLU", "perturbation": "Fairness" } } ], "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 }, { "value": 0.25635087719298244, "description": "min=0.16, mean=0.256, max=0.41, sum=1.282 (5)", "style": { "font-weight": "bold" }, "markdown": false, "run_spec_names": [ "mmlu:subject=abstract_algebra,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=college_chemistry,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=computer_security,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=econometrics,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", "mmlu:subject=us_foreign_policy,method=multiple_choice_joint,model=EleutherAI_pythia-2.8b,data_augmentation=canonical" ] } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/knowledge_fairness.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/knowledge_fairness.json" } ], "name": "fairness" }, { "title": "Bias", "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) - Stereotypes (race)", "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\nStereotypical associations (race, profession): Measures uneven association of racial groups (Asian, Hispanic, White) with target professions. This measurement is based on cooccurence statistics between the racially-associated names (word list from [Garg et al., 2018](https://www.pnas.org/doi/10.1073/pnas.1720347115); race associations based on US Census statistics) and the target professions (word list from [Bolukbasi et al., 2016](https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html)).", "markdown": false, "lower_is_better": true, "metadata": { "metric": "Stereotypes (race)", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "NaturalQuestions (closed-book) - Stereotypes (gender)", "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\nStereotypical associations (gender, profession): Measures uneven association of gender groups (male, female) with target professions. This measurement is based on cooccurence statistics between the gender terms (word list from [Bolukbasi et al., 2016](https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html)) and the target professions (word list from [Bolukbasi et al., 2016](https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html)).", "markdown": false, "lower_is_better": true, "metadata": { "metric": "Stereotypes (gender)", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "NaturalQuestions (closed-book) - Representation (race)", "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\nDemographic representation (race): Measures uneven representation of racial groups (Asian, Hispanic, White). This measurement is based on disparities in the frequency statistics across racially-associated names (word list from [Garg et al., 2018](https://www.pnas.org/doi/10.1073/pnas.1720347115); race associations based on US Census statistics).", "markdown": false, "lower_is_better": true, "metadata": { "metric": "Representation (race)", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "NaturalQuestions (closed-book) - Representation (gender)", "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\nDemographic representation (gender): Measures uneven representation of gender groups (male, female). This measurement is based on disparities in the frequency statistics across gender terms (word list from [Bolukbasi et al., 2016](https://papers.nips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html)).", "markdown": false, "lower_is_better": true, "metadata": { "metric": "Representation (gender)", "run_group": "NaturalQuestions (closed-book)" } } ], "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 } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/knowledge_bias.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/knowledge_bias.json" } ], "name": "bias" }, { "title": "Toxicity", "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) - Toxic fraction", "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\nToxic fraction: Fraction of model outputs that are toxic (based on the PerspectiveAPI toxicity classifier).", "markdown": false, "lower_is_better": true, "metadata": { "metric": "Toxic fraction", "run_group": "NaturalQuestions (closed-book)" } } ], "rows": [ [ { "value": "EleutherAI/pythia-2.8b", "description": "", "markdown": false }, { "markdown": false }, { "description": "No matching runs", "markdown": false } ] ], "links": [ { "text": "LaTeX", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/latex/knowledge_toxicity.tex" }, { "text": "JSON", "href": "benchmark_output/runs/classic_pythia-2.8b-step2000/groups/json/knowledge_toxicity.json" } ], "name": "toxicity" }, { "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" }, { "title": "General information", "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) - # eval", "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\n# eval: Number of evaluation instances.", "markdown": false, "metadata": { "metric": "# eval", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "NaturalQuestions (closed-book) - # train", "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\n# train: Number of training instances (e.g., in-context examples).", "markdown": false, "metadata": { "metric": "# train", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "NaturalQuestions (closed-book) - truncated", "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\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": "NaturalQuestions (closed-book)" } }, { "value": "NaturalQuestions (closed-book) - # prompt tokens", "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\n# prompt tokens: Number of tokens in the prompt.", "markdown": false, "metadata": { "metric": "# prompt tokens", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "NaturalQuestions (closed-book) - # output tokens", "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\n# output tokens: Actual number of output tokens.", "markdown": false, "metadata": { "metric": "# output tokens", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "NaturalQuestions (closed-book) - # trials", "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\n# trials: Number of trials, where in each trial we choose an independent, random set of training instances.", "markdown": false, "metadata": { "metric": "# trials", "run_group": "NaturalQuestions (closed-book)" } }, { "value": "HellaSwag - # eval", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\n# eval: Number of evaluation instances.", "markdown": false, "metadata": { "metric": "# eval", "run_group": "HellaSwag" } }, { "value": "HellaSwag - # train", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\n# train: Number of training instances (e.g., in-context examples).", "markdown": false, "metadata": { "metric": "# train", "run_group": "HellaSwag" } }, { "value": "HellaSwag - truncated", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\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": "HellaSwag" } }, { "value": "HellaSwag - # prompt tokens", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\n# prompt tokens: Number of tokens in the prompt.", "markdown": false, "metadata": { "metric": "# prompt tokens", "run_group": "HellaSwag" } }, { "value": "HellaSwag - # output tokens", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\n# output tokens: Actual number of output tokens.", "markdown": false, "metadata": { "metric": "# output tokens", "run_group": "HellaSwag" } }, { "value": "HellaSwag - # trials", "description": "The HellaSwag benchmark for commonsense reasoning in question answering [(Zellers et al., 2019)](https://aclanthology.org/P19-1472/).\n\n# trials: Number of trials, where in each trial we choose an independent, random set of training instances.", "markdown": false, "metadata": { "metric": "# trials", "run_group": "HellaSwag" } }, { "value": "OpenbookQA - # eval", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\n# eval: Number of evaluation instances.", "markdown": false, "metadata": { "metric": "# eval", "run_group": "OpenbookQA" } }, { "value": "OpenbookQA - # train", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\n# train: Number of training instances (e.g., in-context examples).", "markdown": false, "metadata": { "metric": "# train", "run_group": "OpenbookQA" } }, { "value": "OpenbookQA - truncated", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\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": "OpenbookQA" } }, { "value": "OpenbookQA - # prompt tokens", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\n# prompt tokens: Number of tokens in the prompt.", "markdown": false, "metadata": { "metric": "# prompt tokens", "run_group": "OpenbookQA" } }, { "value": "OpenbookQA - # output tokens", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\n# output tokens: Actual number of output tokens.", "markdown": false, "metadata": { "metric": "# output tokens", "run_group": "OpenbookQA" } }, { "value": "OpenbookQA - # trials", "description": "The OpenbookQA benchmark for commonsense-intensive open book question answering [(Mihaylov et al., 2018)](https://aclanthology.org/D18-1260/).\n\n# trials: Number of trials, where in each trial we choose an independent, random set of training instances.", "markdown": false, "metadata": { "metric": "# trials", "run_group": "OpenbookQA" } }, { "value": "TruthfulQA - # eval", "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\n# eval: Number of evaluation instances.", "markdown": false, "metadata": { "metric": "# eval", "run_group": "TruthfulQA" } }, { "value": "TruthfulQA - # train", "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\n# train: Number of training instances (e.g., in-context examples).", "markdown": false, "metadata": { "metric": "# train", "run_group": "TruthfulQA" } }, { "value": "TruthfulQA - truncated", "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\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": "TruthfulQA" } }, { "value": "TruthfulQA - # prompt tokens", "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\n# prompt tokens: Number of tokens in the prompt.", "markdown": false, "metadata": { "metric": "# prompt tokens", "run_group": "TruthfulQA" } }, { "value": "TruthfulQA - # output tokens", "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\n# output tokens: Actual number of output tokens.", "markdown": false, "metadata": { "metric": "# output tokens", "run_group": "TruthfulQA" } }, { "value": "TruthfulQA - # trials", "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\n# trials: Number of trials, where in each trial we choose an independent, random set of training instances.", "markdown": false, "metadata": { "metric": "# trials", "run_group": "TruthfulQA" } }, { "value": "MMLU - # eval", "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\n# eval: Number of evaluation instances.", "markdown": false, "metadata": { "metric": "# eval", "run_group": "MMLU" } }, { "value": "MMLU - # train", "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\n# train: Number of training instances (e.g., in-context examples).", "markdown": false, "metadata": { "metric": "# train", "run_group": "MMLU" } }, { "value": "MMLU - truncated", "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\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": "MMLU" } }, { "value": "MMLU - # prompt tokens", "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\n# prompt tokens: Number of tokens in the prompt.", "markdown": false, "metadata": { "metric": "# prompt tokens", "run_group": "MMLU" } }, { "value": "MMLU - # output tokens", "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\n# output tokens: Actual number of output tokens.", "markdown": false, "metadata": { "metric": "# output tokens", "run_group": "MMLU" } }, { "value": "MMLU - # trials", "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\n# trials: Number of trials, where in each trial we choose an independent, random set of training instances.", "markdown": false, "metadata": { "metric": "# trials", "run_group": "MMLU" } }, { "value": "WikiFact - # eval", "description": "Scenario introduced in this work, inspired by [Petroni et al. 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