Language Modelling On Lambada

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	Accuracy
massive-language-models-can-be-accurately	0.02
all-nlp-tasks-are-generation-tasks-a-general	72.35
language-models-are-few-shot-learners	86.4
test-time-training-for-out-of-distribution-1	0.01
using-deepspeed-and-megatron-to-train	Megatron-Turing NLG 530B (Few-Shot)
palm-2-technical-report-1	83.7
language-models-are-few-shot-learners	72.5
broad-context-language-modeling-as-reading	49.0
language-models-are-unsupervised-multitask	63.24
language-models-are-few-shot-learners	67.1
pythia-a-suite-for-analyzing-large-language	-
palm-2-technical-report-1	86.9
stay-on-topic-with-classifier-free-guidance	83.9
universal-transformers	56.25
massive-language-models-can-be-accurately	79.47
massive-language-models-can-be-accurately	76.51
palm-scaling-language-modeling-with-pathways-1	77.9
pythia-a-suite-for-analyzing-large-language	67.28
glam-efficient-scaling-of-language-models	80.9
residual-shuffle-exchange-networks-for-fast	54.34
glm-130b-an-open-bilingual-pre-trained-model	80.2
palm-scaling-language-modeling-with-pathways-1	89.7
palm-2-technical-report-1	80.7
Modèle 24	82.33
language-models-are-few-shot-learners	70.3
pythia-a-suite-for-analyzing-large-language	-
stay-on-topic-with-classifier-free-guidance	82.2
stay-on-topic-with-classifier-free-guidance	84.0
language-models-are-few-shot-learners	76.2
Modèle 30	69.7
mamba-linear-time-sequence-modeling-with	69.2
massive-language-models-can-be-accurately	75.59
pythia-a-suite-for-analyzing-large-language	70.46
palm-scaling-language-modeling-with-pathways-1	81.8
all-nlp-tasks-are-generation-tasks-a-general	67.18
training-compute-optimal-large-language	77.7
massive-language-models-can-be-accurately	78.77