Question Answering On Storycloze

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
Switch Transformer 9B	73.3	Efficient Language Modeling with Sparse all-MLP	-
BLOOMZ	96.3	Crosslingual Generalization through Multitask Finetuning
Gshard 9B	67.9	Efficient Language Modeling with Sparse all-MLP	-
SparseGPT (175B, 2:4 Sparsity)	76.19	SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
T0-3B (CoT fine-tuned)	94.5	The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning	-
FLAN 137B (few-shot, k=10)	94.7	Finetuned Language Models Are Zero-Shot Learners
SparseGPT (175B, 50% Sparsity)	78.87	SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
GPT-3 Large 760M (zero-shot)	72.4	Language Models are Few-Shot Learners
FLAN 137B (zero-shot)	93.4	Finetuned Language Models Are Zero-Shot Learners
SparseGPT (175B, 4:8 Sparsity)	77.02	SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
Memory chains and semantic supervision	78.7	-	-
Finetuned Transformer LM	86.5	Improving Language Understanding by Generative Pre-Training
KiC-770M	94.40	Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models	-
val-LS-skip	76.5	A Simple and Effective Approach to the Story Cloze Test	-
sMLP – deterministic 9.4B (0-shot)	74.7	Efficient Language Modeling with Sparse all-MLP	-
Reading Strategies Model	88.3	Improving Machine Reading Comprehension with General Reading Strategies
Hidden Coherence Model	77.6	Story Comprehension for Predicting What Happens Next	-
OPT-175B	79.82	SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
HASH Layers 10B (0-shot)	64.7	Efficient Language Modeling with Sparse all-MLP	-
Base Layers 10B (0-shot)	61.4	Efficient Language Modeling with Sparse all-MLP	-

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