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Models/OPT-175B

OPT-175B

Reported on 16 benchmarks across 4 tasks · 2 papers · 10 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing14 results

Stereotypical Bias AnalysisonCrowS-Pairs
Age· 2022-05-02
67.8
best: 70.1 (LLaMA 65B)
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Stereotypical Bias AnalysisonCrowS-Pairs
Gender· 2022-05-02
65.7
best: 70.6 (LLaMA 65B)
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Stereotypical Bias AnalysisonCrowS-Pairs
Nationality· 2022-05-02
62.9
best: 64.2 (LLaMA 65B)
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Stereotypical Bias AnalysisonCrowS-Pairs
Overall· 2022-05-02
69.5
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Stereotypical Bias AnalysisonCrowS-Pairs
Physical Appearance· 2022-05-02
76.2
best: 77.8 (LLaMA 65B)
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Stereotypical Bias AnalysisonCrowS-Pairs
Race/Color· 2022-05-02
68.6
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Stereotypical Bias AnalysisonCrowS-Pairs
Religion· 2022-05-02
65.7
best: 70.6 (LLaMA 65B)
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Stereotypical Bias AnalysisonCrowS-Pairs
Sexual Orientation· 2022-05-02
78.6
best: 81 (LLaMA 65B)
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Stereotypical Bias AnalysisonCrowS-Pairs
Socioeconomic status· 2022-05-02
76.2
SOTA
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068
Question AnsweringonPIQA
Accuracy· 2023-01-02
81.07
best: 90.1 (Unicorn 11B (fine-tuned))
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot arXiv:2301.00774
Question AnsweringonStoryCloze
Accuracy· 2023-01-02
79.82
best: 96.3 (BLOOMZ)
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot arXiv:2301.00774
Common Sense ReasoningonARC (Challenge)
Accuracy· 2023-01-02
43.94
best: 96.4 (GPT-4 (few-shot, k=25))
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot arXiv:2301.00774
Common Sense ReasoningonARC (Easy)
Accuracy· 2023-01-02
71.04
best: 95.2 (ST-MoE-32B 269B (fine-tuned))
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot arXiv:2301.00774
Stereotypical Bias AnalysisonCrowS-Pairs
Disability· 2022-05-02
76.7
OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068

Medical2 results

Language ModellingonWikiText-2
Test perplexity· uses extra data· 2023-01-02
8.34
best: 8.21 (SparseGPT (175B, 50% Sparsity))
SOTA
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot arXiv:2301.00774
Language ModellingonLAMBADA
Accuracy· 2023-01-02
75.59
best: 89.7 (PaLM-540B (Few-Shot))
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot arXiv:2301.00774