HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Common Sense Reasoning
Common Sense Reasoning On Record
Common Sense Reasoning On Record
Metrics
EM
F1
Results
Performance results of various models on this benchmark
Columns
Model Name
EM
F1
Paper Title
Turing NLR v5 XXL 5.4B (fine-tuned)
95.9
96.4
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
ST-MoE-32B 269B (fine-tuned)
95.1
-
ST-MoE: Designing Stable and Transferable Sparse Expert Models
DeBERTa-1.5B
94.1
94.5
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
PaLM 540B (finetuned)
94.0
94.6
PaLM: Scaling Language Modeling with Pathways
Vega v2 6B (fine-tuned)
93.9
94.4
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
T5-XXL 11B (fine-tuned)
93.4
-
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
GESA 500M
91.7
92.2
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model
LUKE-Graph
91.2
91.5
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension
LUKE (single model)
90.640
91.209
-
LUKE 483M
90.6
91.2
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
KELM (finetuning RoBERTa-large based single model)
89.1
89.6
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs
ST-MoE-L 4.1B (fine-tuned)
88.9
-
ST-MoE: Designing Stable and Transferable Sparse Expert Models
FLAN 137B (prompt-tuned)
85.1
-
Finetuned Language Models Are Zero-Shot Learners
XLNet + MTL + Verifier (ensemble)
83.090
83.737
-
GPT-3 Large 760M (0-shot)
82.1
-
Language Models are Few-Shot Learners
CSRLM (single model)
81.780
82.584
-
XLNet + Verifier
81.5
82.7
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks
XLNet + MTL + Verifier (single model)
81.460
82.664
-
Switch Transformer 9B
79.9
-
Efficient Language Modeling with Sparse all-MLP
{SKG-NET} (single model)
79.480
80.038
-
0 of 45 row(s) selected.
Previous
Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Common Sense Reasoning
Common Sense Reasoning On Record
Common Sense Reasoning On Record
Metrics
EM
F1
Results
Performance results of various models on this benchmark
Columns
Model Name
EM
F1
Paper Title
Turing NLR v5 XXL 5.4B (fine-tuned)
95.9
96.4
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
ST-MoE-32B 269B (fine-tuned)
95.1
-
ST-MoE: Designing Stable and Transferable Sparse Expert Models
DeBERTa-1.5B
94.1
94.5
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
PaLM 540B (finetuned)
94.0
94.6
PaLM: Scaling Language Modeling with Pathways
Vega v2 6B (fine-tuned)
93.9
94.4
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
T5-XXL 11B (fine-tuned)
93.4
-
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
GESA 500M
91.7
92.2
Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model
LUKE-Graph
91.2
91.5
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension
LUKE (single model)
90.640
91.209
-
LUKE 483M
90.6
91.2
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
KELM (finetuning RoBERTa-large based single model)
89.1
89.6
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs
ST-MoE-L 4.1B (fine-tuned)
88.9
-
ST-MoE: Designing Stable and Transferable Sparse Expert Models
FLAN 137B (prompt-tuned)
85.1
-
Finetuned Language Models Are Zero-Shot Learners
XLNet + MTL + Verifier (ensemble)
83.090
83.737
-
GPT-3 Large 760M (0-shot)
82.1
-
Language Models are Few-Shot Learners
CSRLM (single model)
81.780
82.584
-
XLNet + Verifier
81.5
82.7
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks
XLNet + MTL + Verifier (single model)
81.460
82.664
-
Switch Transformer 9B
79.9
-
Efficient Language Modeling with Sparse all-MLP
{SKG-NET} (single model)
79.480
80.038
-
0 of 45 row(s) selected.
Previous
Next