| [ |
| { |
| "title": "Calibration (Detailed)", |
| "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": "MMLU - Max prob", |
| "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\nMax prob: Model's average confidence in its prediction (only computed for classification tasks)", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Max prob", |
| "run_group": "MMLU" |
| } |
| }, |
| { |
| "value": "MMLU - ECE (1-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\n1-bin expected calibration error: The (absolute value) difference between the model's average confidence and accuracy (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "ECE (1-bin)", |
| "run_group": "MMLU" |
| } |
| }, |
| { |
| "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" |
| } |
| }, |
| { |
| "value": "MMLU - Selective Acc", |
| "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\nSelective coverage-accuracy area: The area under the coverage-accuracy curve, a standard selective classification metric (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Selective Acc", |
| "run_group": "MMLU" |
| } |
| }, |
| { |
| "value": "MMLU - Acc@10%", |
| "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\nAccuracy at 10% coverage: The accuracy for the 10% of predictions that the model is most confident on (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Acc@10%", |
| "run_group": "MMLU" |
| } |
| }, |
| { |
| "value": "MMLU - Platt-scaled ECE (1-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\n1-bin expected calibration error (after Platt scaling): 1-bin ECE computed after applying Platt scaling to recalibrate the model's predicted probabilities.", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "Platt-scaled ECE (1-bin)", |
| "run_group": "MMLU" |
| } |
| }, |
| { |
| "value": "MMLU - Platt-scaled 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 (after Platt scaling): 10-bin ECE computed after applying Platt scaling to recalibrate the model's predicted probabilities.", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "Platt-scaled ECE (10-bin)", |
| "run_group": "MMLU" |
| } |
| }, |
| { |
| "value": "MMLU - Platt Coef", |
| "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\nPlatt Scaling Coefficient: Coefficient of the Platt scaling classifier (can compare this across tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Platt Coef", |
| "run_group": "MMLU" |
| } |
| }, |
| { |
| "value": "MMLU - Platt Intercept", |
| "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\nPlatt Scaling Intercept: Intercept of the Platt scaling classifier (can compare this across tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Platt Intercept", |
| "run_group": "MMLU" |
| } |
| }, |
| { |
| "value": "IMDB - Max prob", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nMax prob: Model's average confidence in its prediction (only computed for classification tasks)", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Max prob", |
| "run_group": "IMDB" |
| } |
| }, |
| { |
| "value": "IMDB - ECE (1-bin)", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\n1-bin expected calibration error: The (absolute value) difference between the model's average confidence and accuracy (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "ECE (1-bin)", |
| "run_group": "IMDB" |
| } |
| }, |
| { |
| "value": "IMDB - ECE (10-bin)", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\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": "IMDB" |
| } |
| }, |
| { |
| "value": "IMDB - Selective Acc", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nSelective coverage-accuracy area: The area under the coverage-accuracy curve, a standard selective classification metric (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Selective Acc", |
| "run_group": "IMDB" |
| } |
| }, |
| { |
| "value": "IMDB - Acc@10%", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nAccuracy at 10% coverage: The accuracy for the 10% of predictions that the model is most confident on (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Acc@10%", |
| "run_group": "IMDB" |
| } |
| }, |
| { |
| "value": "IMDB - Platt-scaled ECE (1-bin)", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\n1-bin expected calibration error (after Platt scaling): 1-bin ECE computed after applying Platt scaling to recalibrate the model's predicted probabilities.", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "Platt-scaled ECE (1-bin)", |
| "run_group": "IMDB" |
| } |
| }, |
| { |
| "value": "IMDB - Platt-scaled ECE (10-bin)", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\n10-bin Expected Calibration Error (after Platt scaling): 10-bin ECE computed after applying Platt scaling to recalibrate the model's predicted probabilities.", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "Platt-scaled ECE (10-bin)", |
| "run_group": "IMDB" |
| } |
| }, |
| { |
| "value": "IMDB - Platt Coef", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nPlatt Scaling Coefficient: Coefficient of the Platt scaling classifier (can compare this across tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Platt Coef", |
| "run_group": "IMDB" |
| } |
| }, |
| { |
| "value": "IMDB - Platt Intercept", |
| "description": "The IMDB benchmark for sentiment analysis in movie review [(Maas et al., 2011)](https://aclanthology.org/P11-1015/).\n\nPlatt Scaling Intercept: Intercept of the Platt scaling classifier (can compare this across tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Platt Intercept", |
| "run_group": "IMDB" |
| } |
| }, |
| { |
| "value": "RAFT - Max prob", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\nMax prob: Model's average confidence in its prediction (only computed for classification tasks)", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Max prob", |
| "run_group": "RAFT" |
| } |
| }, |
| { |
| "value": "RAFT - ECE (1-bin)", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\n1-bin expected calibration error: The (absolute value) difference between the model's average confidence and accuracy (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "ECE (1-bin)", |
| "run_group": "RAFT" |
| } |
| }, |
| { |
| "value": "RAFT - ECE (10-bin)", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\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": "RAFT" |
| } |
| }, |
| { |
| "value": "RAFT - Selective Acc", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\nSelective coverage-accuracy area: The area under the coverage-accuracy curve, a standard selective classification metric (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Selective Acc", |
| "run_group": "RAFT" |
| } |
| }, |
| { |
| "value": "RAFT - Acc@10%", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\nAccuracy at 10% coverage: The accuracy for the 10% of predictions that the model is most confident on (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Acc@10%", |
| "run_group": "RAFT" |
| } |
| }, |
| { |
| "value": "RAFT - Platt-scaled ECE (1-bin)", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\n1-bin expected calibration error (after Platt scaling): 1-bin ECE computed after applying Platt scaling to recalibrate the model's predicted probabilities.", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "Platt-scaled ECE (1-bin)", |
| "run_group": "RAFT" |
| } |
| }, |
| { |
| "value": "RAFT - Platt-scaled ECE (10-bin)", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\n10-bin Expected Calibration Error (after Platt scaling): 10-bin ECE computed after applying Platt scaling to recalibrate the model's predicted probabilities.", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "Platt-scaled ECE (10-bin)", |
| "run_group": "RAFT" |
| } |
| }, |
| { |
| "value": "RAFT - Platt Coef", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\nPlatt Scaling Coefficient: Coefficient of the Platt scaling classifier (can compare this across tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Platt Coef", |
| "run_group": "RAFT" |
| } |
| }, |
| { |
| "value": "RAFT - Platt Intercept", |
| "description": "The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks [(Alex et al., 2021)](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html).\n\nPlatt Scaling Intercept: Intercept of the Platt scaling classifier (can compare this across tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Platt Intercept", |
| "run_group": "RAFT" |
| } |
| }, |
| { |
| "value": "CivilComments - Max prob", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\n\nMax prob: Model's average confidence in its prediction (only computed for classification tasks)", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Max prob", |
| "run_group": "CivilComments" |
| } |
| }, |
| { |
| "value": "CivilComments - ECE (1-bin)", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\n\n1-bin expected calibration error: The (absolute value) difference between the model's average confidence and accuracy (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "ECE (1-bin)", |
| "run_group": "CivilComments" |
| } |
| }, |
| { |
| "value": "CivilComments - ECE (10-bin)", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\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": "CivilComments" |
| } |
| }, |
| { |
| "value": "CivilComments - Selective Acc", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\n\nSelective coverage-accuracy area: The area under the coverage-accuracy curve, a standard selective classification metric (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Selective Acc", |
| "run_group": "CivilComments" |
| } |
| }, |
| { |
| "value": "CivilComments - Acc@10%", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\n\nAccuracy at 10% coverage: The accuracy for the 10% of predictions that the model is most confident on (only computed for classification tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Acc@10%", |
| "run_group": "CivilComments" |
| } |
| }, |
| { |
| "value": "CivilComments - Platt-scaled ECE (1-bin)", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\n\n1-bin expected calibration error (after Platt scaling): 1-bin ECE computed after applying Platt scaling to recalibrate the model's predicted probabilities.", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "Platt-scaled ECE (1-bin)", |
| "run_group": "CivilComments" |
| } |
| }, |
| { |
| "value": "CivilComments - Platt-scaled ECE (10-bin)", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\n\n10-bin Expected Calibration Error (after Platt scaling): 10-bin ECE computed after applying Platt scaling to recalibrate the model's predicted probabilities.", |
| "markdown": false, |
| "lower_is_better": true, |
| "metadata": { |
| "metric": "Platt-scaled ECE (10-bin)", |
| "run_group": "CivilComments" |
| } |
| }, |
| { |
| "value": "CivilComments - Platt Coef", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\n\nPlatt Scaling Coefficient: Coefficient of the Platt scaling classifier (can compare this across tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Platt Coef", |
| "run_group": "CivilComments" |
| } |
| }, |
| { |
| "value": "CivilComments - Platt Intercept", |
| "description": "The CivilComments benchmark for toxicity detection [(Borkan et al., 2019)](https://arxiv.org/pdf/1903.04561.pdf).\n\nPlatt Scaling Intercept: Intercept of the Platt scaling classifier (can compare this across tasks).", |
| "markdown": false, |
| "lower_is_better": false, |
| "metadata": { |
| "metric": "Platt Intercept", |
| "run_group": "CivilComments" |
| } |
| } |
| ], |
| "rows": [ |
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| "civil_comments:demographic=black,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "civil_comments:demographic=christian,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "civil_comments:demographic=female,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "civil_comments:demographic=male,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "civil_comments:demographic=muslim,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "civil_comments:demographic=other_religions,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
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| "description": "9 matching runs, but no matching metrics", |
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| "raft:subset=neurips_impact_statement_risks,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "raft:subset=one_stop_english,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "raft:subset=overruling,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "raft:subset=semiconductor_org_types,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "raft:subset=systematic_review_inclusion,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "raft:subset=tai_safety_research,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "raft:subset=terms_of_service,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
| "raft:subset=tweet_eval_hate,model=EleutherAI_pythia-2.8b,data_augmentation=canonical", |
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