Common Sense Reasoning On Record

评估指标

评测结果

各个模型在此基准测试上的表现结果

			Paper Title	Repository
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	-	-

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