Question Answering On Copa

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

		Paper Title	Repository
PaLM 540B (finetuned)	100	PaLM: Scaling Language Modeling with Pathways
Vega v2 6B (KD-based prompt transfer)	99.4	Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE	-
ST-MoE-32B 269B (fine-tuned)	99.2	ST-MoE: Designing Stable and Transferable Sparse Expert Models
UL2 20B (fine-tuned)	99	UL2: Unifying Language Learning Paradigms
DeBERTa-Ensemble	98.4	DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Turing NLR v5 XXL 5.4B (fine-tuned)	98.2	Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE	-
DeBERTa-1.5B	96.8	DeBERTa: Decoding-enhanced BERT with Disentangled Attention
PaLM 2-L (1-shot)	96.0	PaLM 2 Technical Report
T5-XXL 11B (fine-tuned)	94.8	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
FLAN 137B (prompt-tuned)	94	Finetuned Language Models Are Zero-Shot Learners
T5-XL 3B (fine-tuned)	92	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
GPT-3 175B (few-shot, k=32)	92	Language Models are Few-Shot Learners
FLAN 137B (zero-shot)	91	Finetuned Language Models Are Zero-Shot Learners
ST-MoE-L 4.1B (fine-tuned)	91	ST-MoE: Designing Stable and Transferable Sparse Expert Models
GPT-3 175B (0-shot)	91	Language Models are Few-Shot Learners
T0-3B (CoT fine-tuned)	90.9	The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
RoBERTa-Winogrande-ft 355M (fine-tuned)	90.6	WinoGrande: An Adversarial Winograd Schema Challenge at Scale
PaLM 2-M (1-shot)	90.0	PaLM 2 Technical Report
Flipped-3B	89.88	Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
PaLM 2-S (1-shot)	89.0	PaLM 2 Technical Report

0 of 60 row(s) selected.

Command Palette

Question Answering On Copa

评估指标

评测结果