TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Common Sense Reasoning	ARC (Challenge)	ST-MoE-L 4.1B (fine-tuned)	Accuracy	56.9	# 22
Common Sense Reasoning	ARC (Challenge)	ST-MoE-32B 269B (fine-tuned)	Accuracy	86.5	# 10
Common Sense Reasoning	ARC (Easy)	ST-MoE-32B 269B (fine-tuned)	Accuracy	95.2	# 1
Common Sense Reasoning	ARC (Easy)	ST-MoE-L 4.1B (fine-tuned)	Accuracy	75.4	# 19
Question Answering	BoolQ	ST-MoE-32B 269B (fine-tuned)	Accuracy	92.4	# 1
Question Answering	BoolQ	ST-MoE-L 4.1B (fine-tuned)	Accuracy	88.6	# 9
Natural Language Inference	CommitmentBank	ST-MoE-32B 269B (fine-tuned)	Accuracy	98	# 4
Natural Language Inference	CommitmentBank	ST-MoE-L 4.1B (fine-tuned)	Accuracy	98.2	# 3
Question Answering	COPA	ST-MoE-L 4.1B (fine-tuned)	Accuracy	91	# 13
Question Answering	COPA	ST-MoE-32B 269B (fine-tuned)	Accuracy	99.2	# 3
Question Answering	MultiRC	ST-MoE-32B 269B (fine-tuned)	F1	89.6	# 2
Question Answering	MultiRC	ST-MoE-L 4.1B (fine-tuned)	F1	86	# 8
Common Sense Reasoning	ReCoRD	ST-MoE-32B 269B (fine-tuned)	EM	95.1	# 2
Common Sense Reasoning	ReCoRD	ST-MoE-L 4.1B (fine-tuned)	EM	88.9	# 12
Natural Language Inference	RTE	ST-MoE-L 4.1B (fine-tuned)	Accuracy	92.1%	# 10
Natural Language Inference	RTE	ST-MoE-32B 269B (fine-tuned)	Accuracy	93.5%	# 4
Coreference Resolution	Winograd Schema Challenge	ST-MoE-L 4.1B (fine-tuned)	Accuracy	93.3	# 8
Coreference Resolution	Winograd Schema Challenge	ST-MoE-32B 269B (fine-tuned)	Accuracy	96.6	# 5
Common Sense Reasoning	WinoGrande	ST-MoE-32B 269B (fine-tuned)	Accuracy	96.1	# 1
Common Sense Reasoning	WinoGrande	ST-MoE-L 4.1B (fine-tuned)	Accuracy	81.7	# 10
Word Sense Disambiguation	Words in Context	ST-MoE-32B 269B (fine-tuned)	Accuracy	77.7	# 3
Word Sense Disambiguation	Words in Context	ST-MoE-L 4.1B (fine-tuned)	Accuracy	74	# 10

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ST-MoE: Designing Stable and Transferable Sparse Expert Models

17 Feb 2022 · Barret Zoph, Irwan Bello, Sameer Kumar, Nan Du, Yanping Huang, Jeff Dean, Noam Shazeer, William Fedus ·

Scale has opened new frontiers in natural language processing -- but at a high cost. In response, Mixture-of-Experts (MoE) and Switch Transformers have been proposed as an energy efficient path to even larger and more capable language models. But advancing the state-of-the-art across a broad set of natural language tasks has been hindered by training instabilities and uncertain quality during fine-tuning. Our work focuses on these issues and acts as a design guide. We conclude by scaling a sparse model to 269B parameters, with a computational cost comparable to a 32B dense encoder-decoder Transformer (Stable and Transferable Mixture-of-Experts or ST-MoE-32B). For the first time, a sparse model achieves state-of-the-art performance in transfer learning, across a diverse set of tasks including reasoning (SuperGLUE, ARC Easy, ARC Challenge), summarization (XSum, CNN-DM), closed book question answering (WebQA, Natural Questions), and adversarially constructed tasks (Winogrande, ANLI R3).

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tensorflow/mesh official

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xuefuzhao/openmoe

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Tasks

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Common Sense Reasoning

Coreference Resolution

Natural Language Inference

Natural Questions

Question Answering

ST-MoE-L 4.1B (fine-tuned)

Transfer Learning

Word Sense Disambiguation

Datasets

GLUE

SQuAD

Natural Questions

TriviaQA

BoolQ

SuperGLUE

WinoGrande

WSC

COPA

ANLI

MultiRC mC4

ReCoRD

ARC (AI2 Reasoning Challenge) RTE CommitmentBank

Results from the Paper

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Ranked #1 on Common Sense Reasoning on ARC (Easy)

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
Common Sense Reasoning	ARC (Challenge)	ST-MoE-L 4.1B (fine-tuned)	Accuracy	56.9	# 22	Compare
Common Sense Reasoning	ARC (Challenge)	ST-MoE-32B 269B (fine-tuned)	Accuracy	86.5	# 10	Compare
Common Sense Reasoning	ARC (Easy)	ST-MoE-32B 269B (fine-tuned)	Accuracy	95.2	# 1	Compare
Common Sense Reasoning	ARC (Easy)	ST-MoE-L 4.1B (fine-tuned)	Accuracy	75.4	# 19	Compare
Question Answering	BoolQ	ST-MoE-32B 269B (fine-tuned)	Accuracy	92.4	# 1	Compare
Question Answering	BoolQ	ST-MoE-L 4.1B (fine-tuned)	Accuracy	88.6	# 9	Compare
Natural Language Inference	CommitmentBank	ST-MoE-32B 269B (fine-tuned)	Accuracy	98	# 4	Compare
Natural Language Inference	CommitmentBank	ST-MoE-L 4.1B (fine-tuned)	Accuracy	98.2	# 3	Compare
Question Answering	COPA	ST-MoE-L 4.1B (fine-tuned)	Accuracy	91	# 13	Compare
Question Answering	COPA	ST-MoE-32B 269B (fine-tuned)	Accuracy	99.2	# 3	Compare
Question Answering	MultiRC	ST-MoE-32B 269B (fine-tuned)	F1	89.6	# 2	Compare
Question Answering	MultiRC	ST-MoE-L 4.1B (fine-tuned)	F1	86	# 8	Compare
Common Sense Reasoning	ReCoRD	ST-MoE-32B 269B (fine-tuned)	EM	95.1	# 2	Compare
Common Sense Reasoning	ReCoRD	ST-MoE-L 4.1B (fine-tuned)	EM	88.9	# 12	Compare
Natural Language Inference	RTE	ST-MoE-L 4.1B (fine-tuned)	Accuracy	92.1%	# 10	Compare
Natural Language Inference	RTE	ST-MoE-32B 269B (fine-tuned)	Accuracy	93.5%	# 4	Compare
Coreference Resolution	Winograd Schema Challenge	ST-MoE-L 4.1B (fine-tuned)	Accuracy	93.3	# 8	Compare
Coreference Resolution	Winograd Schema Challenge	ST-MoE-32B 269B (fine-tuned)	Accuracy	96.6	# 5	Compare
Common Sense Reasoning	WinoGrande	ST-MoE-32B 269B (fine-tuned)	Accuracy	96.1	# 1	Compare
Common Sense Reasoning	WinoGrande	ST-MoE-L 4.1B (fine-tuned)	Accuracy	81.7	# 10	Compare
Word Sense Disambiguation	Words in Context	ST-MoE-32B 269B (fine-tuned)	Accuracy	77.7	# 3	Compare
Word Sense Disambiguation	Words in Context	ST-MoE-L 4.1B (fine-tuned)	Accuracy	74	# 10	Compare

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Absolute Position Encodings • Adam • BPE • Dense Connections • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer

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