TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Natural Language Inference	ANLI test	PaLM 2-S (one-shot)	A1	53.1	# 10
Natural Language Inference	ANLI test	PaLM 2-S (one-shot)	A2	48.8	# 16
Natural Language Inference	ANLI test	PaLM 2-S (one-shot)	A3	53.2	# 12
Natural Language Inference	ANLI test	PaLM 2-L (one-shot)	A1	73.1	# 4
Natural Language Inference	ANLI test	PaLM 2-L (one-shot)	A2	63.4	# 6
Natural Language Inference	ANLI test	PaLM 2-L (one-shot)	A3	67.1	# 4
Natural Language Inference	ANLI test	PaLM 2-M (one-shot)	A1	58.1	# 9
Natural Language Inference	ANLI test	PaLM 2-M (one-shot)	A2	49.5	# 15
Natural Language Inference	ANLI test	PaLM 2-M (one-shot)	A3	54.5	# 10
Common Sense Reasoning	ARC (Challenge)	PaLM 2-L (1-shot)	Accuracy	69.2	# 17
Common Sense Reasoning	ARC (Challenge)	PaLM 2-M (1-shot)	Accuracy	64.9	# 20
Common Sense Reasoning	ARC (Challenge)	PaLM 2 (few-shot, CoT, SC)	Accuracy	95.1	# 2
Common Sense Reasoning	ARC (Challenge)	PaLM 2-S (1-shot)	Accuracy	59.6	# 23
Common Sense Reasoning	ARC (Easy)	PaLM 2-S (1-shot)	Accuracy	85.6	# 7
Common Sense Reasoning	ARC (Easy)	PaLM 2-M (1-shot)	Accuracy	88.0	# 4
Common Sense Reasoning	ARC (Easy)	PaLM 2-L (1-shot)	Accuracy	89.7	# 3
Common Sense Reasoning	BIG-bench (Causal Judgment)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	58.8	# 3
Common Sense Reasoning	BIG-bench (Causal Judgment)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	62.0	# 1
Common Sense Reasoning	BIG-bench (Date Understanding)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	74.0	# 2
Common Sense Reasoning	BIG-bench (Date Understanding)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	91.2	# 1
Common Sense Reasoning	BIG-bench (Disambiguation QA)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	77.6	# 2
Common Sense Reasoning	BIG-bench (Disambiguation QA)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	78.8	# 1
Logical Reasoning	BIG-bench (Formal Fallacies Syllogisms Negation)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	57.2	# 2
Logical Reasoning	BIG-bench (Formal Fallacies Syllogisms Negation)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	64.8	# 1
Multiple Choice Question Answering (MCQA)	BIG-bench (Hyperbaton)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	84.8	# 5
Multiple Choice Question Answering (MCQA)	BIG-bench (Hyperbaton)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	82.4	# 6
Logical Reasoning	BIG-bench (Logic Grid Puzzle)	PaLM-540B (few-shot, k=5)	Accuracy	42.4	# 2
Logical Reasoning	BIG-bench (Logic Grid Puzzle)	PaLM-62B (few-shot, k=5)	Accuracy	36.5	# 3
Multiple Choice Question Answering (MCQA)	BIG-bench (Movie Recommendation)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	93.6	# 2
Multiple Choice Question Answering (MCQA)	BIG-bench (Movie Recommendation)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	94.4	# 1
Multiple Choice Question Answering (MCQA)	BIG-bench (Navigate)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	91.2	# 1
Multiple Choice Question Answering (MCQA)	BIG-bench (Navigate)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	68.8	# 2
Logical Reasoning	BIG-bench (Penguins In A Table)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	84.9	# 1
Logical Reasoning	BIG-bench (Penguins In A Table)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	65.8	# 2
Logical Reasoning	BIG-bench (Reasoning About Colored Objects)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	91.2	# 1
Logical Reasoning	BIG-bench (Reasoning About Colored Objects)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	61.2	# 2
Multiple Choice Question Answering (MCQA)	BIG-bench (Ruin Names)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	83.6	# 2
Multiple Choice Question Answering (MCQA)	BIG-bench (Ruin Names)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	90	# 1
Sarcasm Detection	BIG-bench (SNARKS)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	78.7	# 2
Sarcasm Detection	BIG-bench (SNARKS)	PaLM 2(few-shot, k=3, CoT)	Accuracy	84.8	# 1
Common Sense Reasoning	BIG-bench (Sports Understanding)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	90.8	# 2
Common Sense Reasoning	BIG-bench (Sports Understanding)	PaLM 2(few-shot, k=3, CoT)	Accuracy	98	# 1
Logical Reasoning	BIG-bench (Temporal Sequences)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	96.4	# 2
Logical Reasoning	BIG-bench (Temporal Sequences)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	100	# 1
Question Answering	BoolQ	PaLM 2-S (1-shot)	Accuracy	88.1	# 12
Question Answering	BoolQ	PaLM 2-L (1-shot)	Accuracy	90.9	# 6
Question Answering	BoolQ	PaLM 2-M (1-shot)	Accuracy	88.6	# 10
Toxic Comment Classification	Civil Comments	PaLM 2 (zero-shot)	AUROC	0.7596	# 17
Toxic Comment Classification	Civil Comments	PaLM 2 (few-shot, k=10)	AUROC	0.8535	# 16
Natural Language Inference	CommitmentBank	PaLM 2-M (one-shot)	Accuracy	80.4	# 12
Natural Language Inference	CommitmentBank	PaLM 2-L (one-shot)	Accuracy	87.5	# 10
Natural Language Inference	CommitmentBank	PaLM 2-S (one-shot)	Accuracy	82.1	# 11
Common Sense Reasoning	CommonsenseQA	PaLM 2 (few‑shot, CoT, SC)	Accuracy	90.4	# 2
Question Answering	COPA	PaLM 2-S (1-shot)	Accuracy	89.0	# 20
Question Answering	COPA	PaLM 2-L (1-shot)	Accuracy	96.0	# 8
Question Answering	COPA	PaLM 2-M (1-shot)	Accuracy	90.0	# 18
Question Answering	DROP Test	PaLM 2 (few-shot)	F1	85.0	# 3
Machine Translation	FRMT (Chinese - Mainland)	PaLM	BLEURT	70.3	# 3
Machine Translation	FRMT (Chinese - Mainland)	PaLM 2	BLEURT	74.4	# 1
Machine Translation	FRMT (Chinese - Mainland)	Google Translate	BLEURT	72.3	# 2
Machine Translation	FRMT (Chinese - Taiwan)	Google Translate	BLEURT	68.5	# 3
Machine Translation	FRMT (Chinese - Taiwan)	PaLM	BLEURT	68.6	# 2
Machine Translation	FRMT (Chinese - Taiwan)	PaLM 2	BLEURT	72.0	# 1
Machine Translation	FRMT (Portuguese - Brazil)	PaLM	BLEURT	78.5	# 3
Machine Translation	FRMT (Portuguese - Brazil)	PaLM 2	BLEURT	81.1	# 1
Machine Translation	FRMT (Portuguese - Brazil)	Google Translate	BLEURT	80.2	# 2
Machine Translation	FRMT (Portuguese - Portugal)	Google Translate	BLEURT	75.3	# 3
Machine Translation	FRMT (Portuguese - Portugal)	PaLM 2	BLEURT	78.3	# 1
Machine Translation	FRMT (Portuguese - Portugal)	PaLM	BLEURT	76.1	# 2
Arithmetic Reasoning	GSM8K	PaLM 2 (few-shot, k=8, SC)	Accuracy	91.0	# 16
Arithmetic Reasoning	GSM8K	PaLM 2 (few-shot, k=8, CoT)	Accuracy	80.7	# 66
Sentence Completion	HellaSwag	PaLM 2-S (1-shot)	Accuracy	85.6	# 22
Sentence Completion	HellaSwag	PaLM 2-M (1-shot)	Accuracy	86.7	# 18
Sentence Completion	HellaSwag	PaLM 2-L (1-shot)	Accuracy	87.4	# 16
Language Modelling	LAMBADA	PaLM 2-S (one-shot)	Accuracy	80.7	# 11
Language Modelling	LAMBADA	PaLM 2-M (one-shot)	Accuracy	83.7	# 6
Language Modelling	LAMBADA	PaLM 2-L (one-shot)	Accuracy	86.9	# 2
Math Word Problem Solving	MATH	PaLM 2 (few-shot, k=4, SC)	Accuracy	48.8	# 49
Math Word Problem Solving	MATH	PaLM 2 (few-shot, k=4, CoT)	Accuracy	34.3	# 79
Code Generation	MBPP	PaLM 2-S* (few-shot)	Accuracy	50	# 53
Multi-task Language Understanding	MGSM	PaLM 2 (few-shot, k=8, SC)	Average (%)	87.0	# 1
Multi-task Language Understanding	MGSM	PaLM 2 (8-shot, CoT)	Average (%)	72.2	# 2
Multi-task Language Understanding	MMLU	PaLM 2-L (5-shot)	Average (%)	78.3	# 18
Multi-task Language Understanding	MMLU	Flan-PaLM 2-L	Average (%)	81.2	# 14
Question Answering	MultiRC	PaLM 2-S (one-shot)	F1	84.0	# 10
Question Answering	MultiRC	PaLM 2-L (one-shot)	F1	88.2	# 4
Question Answering	MultiRC	PaLM 2-M (one-shot)	F1	84.1	# 9
Question Answering	Natural Questions	PaLM 2-L (one-shot)	EM	37.5	# 27
Question Answering	Natural Questions	PaLM 2-M (one-shot)	EM	32.0	# 32
Question Answering	Natural Questions	PaLM 2-S (one-shot)	EM	25.3	# 40
Question Answering	OpenBookQA	PaLM 2-L (1-shot)	Accuracy	58.5	# 30
Question Answering	OpenBookQA	PaLM 2-M (1-shot)	Accuracy	56.2	# 34
Question Answering	OpenBookQA	PaLM 2-S (1-shot)	Accuracy	57.4	# 32
Question Answering	PIQA	PaLM 2-L (1-shot)	Accuracy	85.0	# 11
Question Answering	PIQA	PaLM 2-S (1-shot)	Accuracy	82.2	# 20
Question Answering	PIQA	PaLM 2-M (1-shot)	Accuracy	83.2	# 13
Common Sense Reasoning	ReCoRD	PaLM 2-M (one-shot)	F1	92.4	# 7
Common Sense Reasoning	ReCoRD	PaLM 2-L (one-shot)	F1	93.8	# 6
Common Sense Reasoning	ReCoRD	PaLM 2-S (one-shot)	F1	92.1	# 9
Natural Language Inference	RTE	PaLM 2-S (1-shot)	Accuracy	78.7%	# 41
Natural Language Inference	RTE	PaLM 2-M (1-shot)	Accuracy	81.9%	# 33
Natural Language Inference	RTE	PaLM 2-L (1-shot)	Accuracy	79.3%	# 39
Question Answering	Story Cloze	PaLM 2-S (one-shot)	Accuracy	85.6	# 5
Question Answering	Story Cloze	PaLM 2-L (one-shot)	Accuracy	87.4	# 3
Question Answering	Story Cloze	PaLM 2-M (one-shot)	Accuracy	86.7	# 4
Question Answering	StrategyQA	PaLM 2 (few-shot, CoT, SC)	Accuracy	90.4	# 1
Question Answering	TriviaQA	PaLM 2-L (one-shot)	EM	86.1	# 5
Question Answering	TriviaQA	PaLM 2-M (one-shot)	EM	81.7	# 10
Question Answering	TriviaQA	PaLM 2-S (one-shot)	EM	75.2	# 22
Cross-Lingual Question Answering	TyDiQA-GoldP	PaLM 2-S (one-shot)	F1	73.3	# 4
Cross-Lingual Question Answering	TyDiQA-GoldP	PaLM 2-L (one-shot)	F1	73.6	# 3
Cross-Lingual Question Answering	TyDiQA-GoldP	PaLM 2-M (one-shot)	F1	73.3	# 4
Question Answering	WebQuestions	PaLM 2-M (one-shot)	EM	26.9	# 12
Question Answering	WebQuestions	PaLM 2-L (one-shot)	EM	28.2	# 11
Question Answering	WebQuestions	PaLM 2-S (one-shot)	EM	21.8	# 15
Coreference Resolution	Winograd Schema Challenge	PaLM 2-S (1-shot)	Accuracy	84.6	# 17
Coreference Resolution	Winograd Schema Challenge	PaLM 2-L (1-shot)	Accuracy	86.9	# 14
Coreference Resolution	Winograd Schema Challenge	PaLM 2-M (1-shot)	Accuracy	88.1	# 13
Common Sense Reasoning	WinoGrande	PaLM 2-L (1-shot)	Accuracy	83.0	# 11
Common Sense Reasoning	WinoGrande	PaLM 2-M (1-shot)	Accuracy	79.2	# 17
Common Sense Reasoning	WinoGrande	PaLM 2-S (1-shot)	Accuracy	77.9	# 19
Word Sense Disambiguation	Words in Context	PaLM 2-M (one-shot)	Accuracy	52.0	# 28
Word Sense Disambiguation	Words in Context	PaLM 2-S (one-shot)	Accuracy	50.6	# 31
Word Sense Disambiguation	Words in Context	PaLM 2-L (one-shot)	Accuracy	66.8	# 13
Cross-Lingual Transfer	XCOPA	PaLM 2 (few-shot)	Accuracy	94.4	# 1
Text Summarization	X-Sum	PaLM 2-M (one-shot)	ROUGE-2	17.2	# 9
Text Summarization	X-Sum	PaLM 2-S (one-shot)	ROUGE-2	16.9	# 10
Text Summarization	X-Sum	PaLM 2-L (one-shot)	ROUGE-2	23.2	# 6

Badge	Markdown
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/common-sense-reasoning-on-big-bench-causal)](https://paperswithcode.com/sota/common-sense-reasoning-on-big-bench-causal?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/common-sense-reasoning-on-big-bench-date)](https://paperswithcode.com/sota/common-sense-reasoning-on-big-bench-date?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/common-sense-reasoning-on-big-bench)](https://paperswithcode.com/sota/common-sense-reasoning-on-big-bench?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/logical-reasoning-on-big-bench-formal)](https://paperswithcode.com/sota/logical-reasoning-on-big-bench-formal?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/multiple-choice-question-answering-mcqa-on-28)](https://paperswithcode.com/sota/multiple-choice-question-answering-mcqa-on-28?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/multiple-choice-question-answering-mcqa-on-29)](https://paperswithcode.com/sota/multiple-choice-question-answering-mcqa-on-29?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/logical-reasoning-on-big-bench-penguins-in-a)](https://paperswithcode.com/sota/logical-reasoning-on-big-bench-penguins-in-a?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/logical-reasoning-on-big-bench-reasoning)](https://paperswithcode.com/sota/logical-reasoning-on-big-bench-reasoning?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/multiple-choice-question-answering-mcqa-on-30)](https://paperswithcode.com/sota/multiple-choice-question-answering-mcqa-on-30?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/sarcasm-detection-on-big-bench-snarks)](https://paperswithcode.com/sota/sarcasm-detection-on-big-bench-snarks?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/common-sense-reasoning-on-big-bench-sports)](https://paperswithcode.com/sota/common-sense-reasoning-on-big-bench-sports?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/logical-reasoning-on-big-bench-temporal)](https://paperswithcode.com/sota/logical-reasoning-on-big-bench-temporal?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/machine-translation-on-frmt-chinese-mainland)](https://paperswithcode.com/sota/machine-translation-on-frmt-chinese-mainland?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/machine-translation-on-frmt-chinese-taiwan)](https://paperswithcode.com/sota/machine-translation-on-frmt-chinese-taiwan?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/machine-translation-on-frmt-portuguese-brazil)](https://paperswithcode.com/sota/machine-translation-on-frmt-portuguese-brazil?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/machine-translation-on-frmt-portuguese)](https://paperswithcode.com/sota/machine-translation-on-frmt-portuguese?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/multi-task-language-understanding-on-mgsm)](https://paperswithcode.com/sota/multi-task-language-understanding-on-mgsm?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-strategyqa)](https://paperswithcode.com/sota/question-answering-on-strategyqa?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/cross-lingual-transfer-on-xcopa)](https://paperswithcode.com/sota/cross-lingual-transfer-on-xcopa?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/common-sense-reasoning-on-arc-challenge)](https://paperswithcode.com/sota/common-sense-reasoning-on-arc-challenge?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/logical-reasoning-on-big-bench-logic-grid)](https://paperswithcode.com/sota/logical-reasoning-on-big-bench-logic-grid?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/common-sense-reasoning-on-commonsenseqa)](https://paperswithcode.com/sota/common-sense-reasoning-on-commonsenseqa?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/language-modelling-on-lambada)](https://paperswithcode.com/sota/language-modelling-on-lambada?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/common-sense-reasoning-on-arc-easy)](https://paperswithcode.com/sota/common-sense-reasoning-on-arc-easy?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-drop-test)](https://paperswithcode.com/sota/question-answering-on-drop-test?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-story-cloze)](https://paperswithcode.com/sota/question-answering-on-story-cloze?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/cross-lingual-question-answering-on-tydiqa)](https://paperswithcode.com/sota/cross-lingual-question-answering-on-tydiqa?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/natural-language-inference-on-anli-test)](https://paperswithcode.com/sota/natural-language-inference-on-anli-test?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-multirc)](https://paperswithcode.com/sota/question-answering-on-multirc?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/multiple-choice-question-answering-mcqa-on-27)](https://paperswithcode.com/sota/multiple-choice-question-answering-mcqa-on-27?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-triviaqa)](https://paperswithcode.com/sota/question-answering-on-triviaqa?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-boolq)](https://paperswithcode.com/sota/question-answering-on-boolq?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/common-sense-reasoning-on-record)](https://paperswithcode.com/sota/common-sense-reasoning-on-record?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/text-summarization-on-x-sum)](https://paperswithcode.com/sota/text-summarization-on-x-sum?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-copa)](https://paperswithcode.com/sota/question-answering-on-copa?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/natural-language-inference-on-commitmentbank)](https://paperswithcode.com/sota/natural-language-inference-on-commitmentbank?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-piqa)](https://paperswithcode.com/sota/question-answering-on-piqa?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/question-answering-on-webquestions)](https://paperswithcode.com/sota/question-answering-on-webquestions?p=palm-2-technical-report-1)`
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	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/coreference-resolution-on-winograd-schema)](https://paperswithcode.com/sota/coreference-resolution-on-winograd-schema?p=palm-2-technical-report-1)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/palm-2-technical-report-1/word-sense-disambiguation-on-words-in-context)](https://paperswithcode.com/sota/word-sense-disambiguation-on-words-in-context?p=palm-2-technical-report-1)`
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PaLM 2 Technical Report

17 May 2023 · Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H. Clark, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vlad Feinberg, Fangxiaoyu Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas Gonzalez, Guy Gur-Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, Andrea Hu, Jeffrey Hui, Jeremy Hurwitz, Michael Isard, Abe Ittycheriah, Matthew Jagielski, Wenhao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Benjamin Lee, Eric Li, Music Li, Wei Li, Yaguang Li, Jian Li, Hyeontaek Lim, Hanzhao Lin, Zhongtao Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nystrom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif, Bryan Richter, Parker Riley, Alex Castro Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Shelby, Ambrose Slone, Daniel Smilkov, David R. So, Daniel Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, ZiRui Wang, Tao Wang, John Wieting, Yuhuai Wu, Kelvin Xu, Yunhan Xu, Linting Xue, Pengcheng Yin, Jiahui Yu, Qiao Zhang, Steven Zheng, Ce Zheng, Weikang Zhou, Denny Zhou, Slav Petrov, Yonghui Wu ·

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.

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Tasks

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Code Generation

Common Sense Reasoning

Coreference Resolution

Language Modelling

Multi-task Language Understanding

Natural Language Inference

Question Answering

Sentence Completion

Datasets

GLUE

MMLU

Natural Questions

GSM8K

TriviaQA

HumanEval

HellaSwag

MATH

PIQA

BoolQ

WinoGrande

OpenBookQA MBPP

CommonsenseQA

RACE

WSC

DROP

COPA

BIG-bench

ANLI

LAMBADA BBH

StrategyQA

WebQuestions

MultiRC Civil Comments

ARC (AI2 Reasoning Challenge)

ReCoRD XCOPA BBQ MGSM

XL-Sum RTE

WikiLingua

TyDiQA-GoldP

XSum CommitmentBank

FRMT

Results from the Paper

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
Natural Language Inference	ANLI test	PaLM 2-S (one-shot)	A1	53.1	# 10	Compare
			A2	48.8	# 16	Compare
			A3	53.2	# 12	Compare
Natural Language Inference	ANLI test	PaLM 2-L (one-shot)	A1	73.1	# 4	Compare
			A2	63.4	# 6	Compare
			A3	67.1	# 4	Compare
Natural Language Inference	ANLI test	PaLM 2-M (one-shot)	A1	58.1	# 9	Compare
			A2	49.5	# 15	Compare
			A3	54.5	# 10	Compare
Common Sense Reasoning	ARC (Challenge)	PaLM 2-L (1-shot)	Accuracy	69.2	# 17	Compare
Common Sense Reasoning	ARC (Challenge)	PaLM 2-M (1-shot)	Accuracy	64.9	# 20	Compare
Common Sense Reasoning	ARC (Challenge)	PaLM 2 (few-shot, CoT, SC)	Accuracy	95.1	# 2	Compare
Common Sense Reasoning	ARC (Challenge)	PaLM 2-S (1-shot)	Accuracy	59.6	# 23	Compare
Common Sense Reasoning	ARC (Easy)	PaLM 2-S (1-shot)	Accuracy	85.6	# 7	Compare
Common Sense Reasoning	ARC (Easy)	PaLM 2-M (1-shot)	Accuracy	88.0	# 4	Compare
Common Sense Reasoning	ARC (Easy)	PaLM 2-L (1-shot)	Accuracy	89.7	# 3	Compare
Common Sense Reasoning	BIG-bench (Causal Judgment)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	58.8	# 3	Compare
Common Sense Reasoning	BIG-bench (Causal Judgment)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	62.0	# 1	Compare
Common Sense Reasoning	BIG-bench (Date Understanding)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	74.0	# 2	Compare
Common Sense Reasoning	BIG-bench (Date Understanding)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	91.2	# 1	Compare
Common Sense Reasoning	BIG-bench (Disambiguation QA)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	77.6	# 2	Compare
Common Sense Reasoning	BIG-bench (Disambiguation QA)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	78.8	# 1	Compare
Logical Reasoning	BIG-bench (Formal Fallacies Syllogisms Negation)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	57.2	# 2	Compare
Logical Reasoning	BIG-bench (Formal Fallacies Syllogisms Negation)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	64.8	# 1	Compare
Multiple Choice Question Answering (MCQA)	BIG-bench (Hyperbaton)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	84.8	# 5	Compare
Multiple Choice Question Answering (MCQA)	BIG-bench (Hyperbaton)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	82.4	# 6	Compare
Logical Reasoning	BIG-bench (Logic Grid Puzzle)	PaLM-540B (few-shot, k=5)	Accuracy	42.4	# 2	Compare
Logical Reasoning	BIG-bench (Logic Grid Puzzle)	PaLM-62B (few-shot, k=5)	Accuracy	36.5	# 3	Compare
Multiple Choice Question Answering (MCQA)	BIG-bench (Movie Recommendation)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	93.6	# 2	Compare
Multiple Choice Question Answering (MCQA)	BIG-bench (Movie Recommendation)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	94.4	# 1	Compare
Multiple Choice Question Answering (MCQA)	BIG-bench (Navigate)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	91.2	# 1	Compare
Multiple Choice Question Answering (MCQA)	BIG-bench (Navigate)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	68.8	# 2	Compare
Logical Reasoning	BIG-bench (Penguins In A Table)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	84.9	# 1	Compare
Logical Reasoning	BIG-bench (Penguins In A Table)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	65.8	# 2	Compare
Logical Reasoning	BIG-bench (Reasoning About Colored Objects)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	91.2	# 1	Compare
Logical Reasoning	BIG-bench (Reasoning About Colored Objects)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	61.2	# 2	Compare
Multiple Choice Question Answering (MCQA)	BIG-bench (Ruin Names)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	83.6	# 2	Compare
Multiple Choice Question Answering (MCQA)	BIG-bench (Ruin Names)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	90	# 1	Compare
Sarcasm Detection	BIG-bench (SNARKS)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	78.7	# 2	Compare
Sarcasm Detection	BIG-bench (SNARKS)	PaLM 2(few-shot, k=3, CoT)	Accuracy	84.8	# 1	Compare
Common Sense Reasoning	BIG-bench (Sports Understanding)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	90.8	# 2	Compare
Common Sense Reasoning	BIG-bench (Sports Understanding)	PaLM 2(few-shot, k=3, CoT)	Accuracy	98	# 1	Compare
Logical Reasoning	BIG-bench (Temporal Sequences)	PaLM 2 (few-shot, k=3, Direct)	Accuracy	96.4	# 2	Compare
Logical Reasoning	BIG-bench (Temporal Sequences)	PaLM 2 (few-shot, k=3, CoT)	Accuracy	100	# 1	Compare
Question Answering	BoolQ	PaLM 2-S (1-shot)	Accuracy	88.1	# 12	Compare
Question Answering	BoolQ	PaLM 2-L (1-shot)	Accuracy	90.9	# 6	Compare
Question Answering	BoolQ	PaLM 2-M (1-shot)	Accuracy	88.6	# 10	Compare
Toxic Comment Classification	Civil Comments	PaLM 2 (zero-shot)	AUROC	0.7596	# 17	Compare
Toxic Comment Classification	Civil Comments	PaLM 2 (few-shot, k=10)	AUROC	0.8535	# 16	Compare
Natural Language Inference	CommitmentBank	PaLM 2-M (one-shot)	Accuracy	80.4	# 12	Compare
Natural Language Inference	CommitmentBank	PaLM 2-L (one-shot)	Accuracy	87.5	# 10	Compare
Natural Language Inference	CommitmentBank	PaLM 2-S (one-shot)	Accuracy	82.1	# 11	Compare
Common Sense Reasoning	CommonsenseQA	PaLM 2 (few‑shot, CoT, SC)	Accuracy	90.4	# 2	Compare
Question Answering	COPA	PaLM 2-S (1-shot)	Accuracy	89.0	# 20	Compare
Question Answering	COPA	PaLM 2-L (1-shot)	Accuracy	96.0	# 8	Compare
Question Answering	COPA	PaLM 2-M (1-shot)	Accuracy	90.0	# 18	Compare
Question Answering	DROP Test	PaLM 2 (few-shot)	F1	85.0	# 3	Compare
Machine Translation	FRMT (Chinese - Mainland)	PaLM	BLEURT	70.3	# 3	Compare
Machine Translation	FRMT (Chinese - Mainland)	PaLM 2	BLEURT	74.4	# 1	Compare
Machine Translation	FRMT (Chinese - Mainland)	Google Translate	BLEURT	72.3	# 2	Compare
Machine Translation	FRMT (Chinese - Taiwan)	Google Translate	BLEURT	68.5	# 3	Compare
Machine Translation	FRMT (Chinese - Taiwan)	PaLM	BLEURT	68.6	# 2	Compare
Machine Translation	FRMT (Chinese - Taiwan)	PaLM 2	BLEURT	72.0	# 1	Compare
Machine Translation	FRMT (Portuguese - Brazil)	PaLM	BLEURT	78.5	# 3	Compare
Machine Translation	FRMT (Portuguese - Brazil)	PaLM 2	BLEURT	81.1	# 1	Compare
Machine Translation	FRMT (Portuguese - Brazil)	Google Translate	BLEURT	80.2	# 2	Compare
Machine Translation	FRMT (Portuguese - Portugal)	Google Translate	BLEURT	75.3	# 3	Compare
Machine Translation	FRMT (Portuguese - Portugal)	PaLM 2	BLEURT	78.3	# 1	Compare
Machine Translation	FRMT (Portuguese - Portugal)	PaLM	BLEURT	76.1	# 2	Compare
Arithmetic Reasoning	GSM8K	PaLM 2 (few-shot, k=8, SC)	Accuracy	91.0	# 16	Compare
Arithmetic Reasoning	GSM8K	PaLM 2 (few-shot, k=8, CoT)	Accuracy	80.7	# 66	Compare
Sentence Completion	HellaSwag	PaLM 2-S (1-shot)	Accuracy	85.6	# 22	Compare
Sentence Completion	HellaSwag	PaLM 2-M (1-shot)	Accuracy	86.7	# 18	Compare
Sentence Completion	HellaSwag	PaLM 2-L (1-shot)	Accuracy	87.4	# 16	Compare
Language Modelling	LAMBADA	PaLM 2-S (one-shot)	Accuracy	80.7	# 11	Compare
Language Modelling	LAMBADA	PaLM 2-M (one-shot)	Accuracy	83.7	# 6	Compare
Language Modelling	LAMBADA	PaLM 2-L (one-shot)	Accuracy	86.9	# 2	Compare
Math Word Problem Solving	MATH	PaLM 2 (few-shot, k=4, SC)	Accuracy	48.8	# 49	Compare
Math Word Problem Solving	MATH	PaLM 2 (few-shot, k=4, CoT)	Accuracy	34.3	# 79	Compare
Code Generation	MBPP	PaLM 2-S* (few-shot)	Accuracy	50	# 53	Compare
Multi-task Language Understanding	MGSM	PaLM 2 (few-shot, k=8, SC)	Average (%)	87.0	# 1	Compare
Multi-task Language Understanding	MGSM	PaLM 2 (8-shot, CoT)	Average (%)	72.2	# 2	Compare
Multi-task Language Understanding	MMLU	PaLM 2-L (5-shot)	Average (%)	78.3	# 18	Compare
Multi-task Language Understanding	MMLU	Flan-PaLM 2-L	Average (%)	81.2	# 14	Compare
Question Answering	MultiRC	PaLM 2-S (one-shot)	F1	84.0	# 10	Compare
Question Answering	MultiRC	PaLM 2-L (one-shot)	F1	88.2	# 4	Compare
Question Answering	MultiRC	PaLM 2-M (one-shot)	F1	84.1	# 9	Compare
Question Answering	Natural Questions	PaLM 2-L (one-shot)	EM	37.5	# 27	Compare
Question Answering	Natural Questions	PaLM 2-M (one-shot)	EM	32.0	# 32	Compare
Question Answering	Natural Questions	PaLM 2-S (one-shot)	EM	25.3	# 40	Compare
Question Answering	OpenBookQA	PaLM 2-L (1-shot)	Accuracy	58.5	# 30	Compare
Question Answering	OpenBookQA	PaLM 2-M (1-shot)	Accuracy	56.2	# 34	Compare
Question Answering	OpenBookQA	PaLM 2-S (1-shot)	Accuracy	57.4	# 32	Compare
Question Answering	PIQA	PaLM 2-L (1-shot)	Accuracy	85.0	# 11	Compare
Question Answering	PIQA	PaLM 2-S (1-shot)	Accuracy	82.2	# 20	Compare
Question Answering	PIQA	PaLM 2-M (1-shot)	Accuracy	83.2	# 13	Compare
Common Sense Reasoning	ReCoRD	PaLM 2-M (one-shot)	F1	92.4	# 7	Compare
Common Sense Reasoning	ReCoRD	PaLM 2-L (one-shot)	F1	93.8	# 6	Compare
Common Sense Reasoning	ReCoRD	PaLM 2-S (one-shot)	F1	92.1	# 9	Compare
Natural Language Inference	RTE	PaLM 2-S (1-shot)	Accuracy	78.7%	# 41	Compare
Natural Language Inference	RTE	PaLM 2-M (1-shot)	Accuracy	81.9%	# 33	Compare
Natural Language Inference	RTE	PaLM 2-L (1-shot)	Accuracy	79.3%	# 39	Compare
Question Answering	Story Cloze	PaLM 2-S (one-shot)	Accuracy	85.6	# 5	Compare
Question Answering	Story Cloze	PaLM 2-L (one-shot)	Accuracy	87.4	# 3	Compare
Question Answering	Story Cloze	PaLM 2-M (one-shot)	Accuracy	86.7	# 4	Compare
Question Answering	StrategyQA	PaLM 2 (few-shot, CoT, SC)	Accuracy	90.4	# 1	Compare
Question Answering	TriviaQA	PaLM 2-L (one-shot)	EM	86.1	# 5	Compare
Question Answering	TriviaQA	PaLM 2-M (one-shot)	EM	81.7	# 10	Compare
Question Answering	TriviaQA	PaLM 2-S (one-shot)	EM	75.2	# 22	Compare
Cross-Lingual Question Answering	TyDiQA-GoldP	PaLM 2-S (one-shot)	F1	73.3	# 4	Compare
Cross-Lingual Question Answering	TyDiQA-GoldP	PaLM 2-L (one-shot)	F1	73.6	# 3	Compare
Cross-Lingual Question Answering	TyDiQA-GoldP	PaLM 2-M (one-shot)	F1	73.3	# 4	Compare
Question Answering	WebQuestions	PaLM 2-M (one-shot)	EM	26.9	# 12	Compare
Question Answering	WebQuestions	PaLM 2-L (one-shot)	EM	28.2	# 11	Compare
Question Answering	WebQuestions	PaLM 2-S (one-shot)	EM	21.8	# 15	Compare
Coreference Resolution	Winograd Schema Challenge	PaLM 2-S (1-shot)	Accuracy	84.6	# 17	Compare
Coreference Resolution	Winograd Schema Challenge	PaLM 2-L (1-shot)	Accuracy	86.9	# 14	Compare
Coreference Resolution	Winograd Schema Challenge	PaLM 2-M (1-shot)	Accuracy	88.1	# 13	Compare
Common Sense Reasoning	WinoGrande	PaLM 2-L (1-shot)	Accuracy	83.0	# 11	Compare
Common Sense Reasoning	WinoGrande	PaLM 2-M (1-shot)	Accuracy	79.2	# 17	Compare
Common Sense Reasoning	WinoGrande	PaLM 2-S (1-shot)	Accuracy	77.9	# 19	Compare
Word Sense Disambiguation	Words in Context	PaLM 2-M (one-shot)	Accuracy	52.0	# 28	Compare
Word Sense Disambiguation	Words in Context	PaLM 2-S (one-shot)	Accuracy	50.6	# 31	Compare
Word Sense Disambiguation	Words in Context	PaLM 2-L (one-shot)	Accuracy	66.8	# 13	Compare
Cross-Lingual Transfer	XCOPA	PaLM 2 (few-shot)	Accuracy	94.4	# 1	Compare
Text Summarization	X-Sum	PaLM 2-M (one-shot)	ROUGE-2	17.2	# 9	Compare
Text Summarization	X-Sum	PaLM 2-S (one-shot)	ROUGE-2	16.9	# 10

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