Arithmetic Reasoning On Gsm8K

المقاييس

Accuracy

Parameters (Billion)

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

جدول المقارنة

اسم النموذج	Accuracy	Parameters (Billion)
lever-learning-to-verify-language-to-code	84.5	175
composing-ensembles-of-pre-trained-models-via	16.8	0.355
tora-a-tool-integrated-reasoning-agent-for	72.6	7
openmathinstruct-1-a-1-8-million-math	75.9	7
gemini-a-family-of-highly-capable-multimodal-1	86.5	-
transcending-scaling-laws-with-0-1-extra	58.5	540
mathcoder-seamless-code-integration-in-llms	74.1	7
openmathinstruct-2-accelerating-ai-for-math	94.9	-
the-art-of-llm-refinement-ask-refine-and	82.6	-
parameter-efficient-sparsity-crafting-from	78.3	-
orca-math-unlocking-the-potential-of-slms-in	86.8	7
large-language-models-can-self-improve	74.4	540
llama-open-and-efficient-foundation-language-1	17.8	13
an-empirical-study-of-data-ability-boundary	73.9	7
outcome-supervised-verifiers-for-planning-in	84.7	7
solving-quantitative-reasoning-problems-with	89	62
النموذج 17	95.2	75
on-the-advance-of-making-language-models	83.2	175
llama-open-and-efficient-foundation-language-1	53.1	33
dart-math-difficulty-aware-rejection-tuning-1	82.5	8
model-card-and-evaluations-for-claude-models	85.2	-
sparks-of-artificial-general-intelligence	87.1	-
wizardmath-empowering-mathematical-reasoning	63.9	13
large-language-models-can-self-improve	32.2	540
wizardmath-empowering-mathematical-reasoning	83.2	7
openmathinstruct-1-a-1-8-million-math	86.8	13
palm-2-technical-report-1	80.7	-
the-claude-3-model-family-opus-sonnet-haiku	92.3	-
large-language-models-are-zero-shot-reasoners	51.5	175
orca-2-teaching-small-language-models-how-to	59.14	13
scaling-relationship-on-learning-mathematical	51.2	7
solving-math-word-problems-with-process-and	76.5	70
palm-2-technical-report-1	91.0	-
النموذج 34	80.2	-
openmathinstruct-2-accelerating-ai-for-math	96.0	-
solving-math-word-problems-with-process-and	87.1	70
النموذج 37	66.9	-
mathcoder-seamless-code-integration-in-llms	67.8	7
tora-a-tool-integrated-reasoning-agent-for	84.3	70
an-empirical-study-of-data-ability-boundary	80.5	7
large-language-models-are-zero-shot-reasoners	17.9	540
dart-math-difficulty-aware-rejection-tuning-1	82.6	7
solving-quantitative-reasoning-problems-with	56.5	540
large-language-models-are-zero-shot-reasoners	55.0	175
scaling-relationship-on-learning-mathematical	64.8	79
النموذج 46	96.4	7
metamath-bootstrap-your-own-mathematical	82.3	70
large-language-models-are-zero-shot-reasoners	40.7	175
hierarchical-prompting-taxonomy-a-universal	97.72	-
large-language-models-can-self-improve	82.1	540
openmathinstruct-1-a-1-8-million-math	90.8	70
النموذج 52	71.9	-
least-to-most-prompting-enables-complex	68.01	175
dart-math-difficulty-aware-rejection-tuning-1	90.4	70
teaching-inspired-integrated-prompting	94.8	-
solving-quantitative-reasoning-problems-with	28.4	8
openchat-advancing-open-source-language	77.3	7
large-language-models-are-zero-shot-reasoners	10.4	175
model-card-and-evaluations-for-claude-models	88	-
composing-ensembles-of-pre-trained-models-via	18.3	0.355
openmathinstruct-1-a-1-8-million-math	80.7	34
النموذج 62	76.4	7
openmathinstruct-1-a-1-8-million-math	84.6	70
openmathinstruct-1-a-1-8-million-math	78.8	13
wizardmath-empowering-mathematical-reasoning	81.6	70
orca-2-teaching-small-language-models-how-to	47.23	7
solving-quantitative-reasoning-problems-with	56.8	8
tora-a-tool-integrated-reasoning-agent-for	80.7	34
dart-math-difficulty-aware-rejection-tuning-1	88.2	7
deepseekmath-pushing-the-limits-of	88.2	7
an-empirical-study-of-data-ability-boundary	80.4	34
openmathinstruct-1-a-1-8-million-math	90.1	70
llama-open-and-efficient-foundation-language-1	11.0	7
the-claude-3-model-family-opus-sonnet-haiku	88.9	-
qwen2-technical-report	96.7	72
large-language-models-are-zero-shot-reasoners	58.1	540
solving-quantitative-reasoning-problems-with	52.4	62
composing-ensembles-of-pre-trained-models-via	12.2	0.355
large-language-models-can-self-improve	56.5	540
openmathinstruct-2-accelerating-ai-for-math	91.7	-
the-unreasonable-effectiveness-of-eccentric	43	13
solving-quantitative-reasoning-problems-with	4.1	8
llama-open-and-efficient-foundation-language-1	35.6	33
openmathinstruct-2-accelerating-ai-for-math	94.1	-
النموذج 85	93.2	7
النموذج 86	89.0	13
gpt-4-technical-report-1	57.1	-
llama-open-and-efficient-foundation-language-1	18.1	7
النموذج 89	94.13	7
frugal-lms-trained-to-invoke-symbolic-solvers	35.2	13
openmathinstruct-1-a-1-8-million-math	84.8	7
kwaiyiimath-technical-report	73.3	13
dart-math-difficulty-aware-rejection-tuning-1	81.1	7
unifying-language-learning-paradigms	4.4	20
openmathinstruct-1-a-1-8-million-math	80.2	7
large-language-models-can-self-improve	73.5	540
tora-a-tool-integrated-reasoning-agent-for	75.8	13
solving-quantitative-reasoning-problems-with	16.2	8
tinygsm-achieving-80-on-gsm8k-with-small	81.5	2.6
large-language-models-are-zero-shot-reasoners	41.3	175
llama-2-open-foundation-and-fine-tuned-chat	56.8	70
the-unreasonable-effectiveness-of-eccentric	41	7
learning-from-self-sampled-correct-and	19.5	2.7
mathcoder-seamless-code-integration-in-llms	72.6	13
math-shepherd-a-label-free-step-by-step	89.1	7
scaling-relationship-on-learning-mathematical	55.3	13
outcome-supervised-verifiers-for-planning-in	73.7	7
llama-open-and-efficient-foundation-language-1	50.9	65
metamath-bootstrap-your-own-mathematical	77.7	7
large-language-models-can-self-improve	17.9	540
codet5-open-code-large-language-models-for	73.8	0.77
query-and-response-augmentation-cannot-help	69.8	7
solving-math-word-problems-with-process-and	87.3	70
النموذج 114	95.1	7
metamath-bootstrap-your-own-mathematical	66.4	7
openmathinstruct-1-a-1-8-million-math	84.7	70
model-card-and-evaluations-for-claude-models	80.9	-
math-shepherd-a-label-free-step-by-step	84.1	7
self-consistency-improves-chain-of-thought	74.4	540
query-and-response-augmentation-cannot-help	82.3	70
llemma-an-open-language-model-for-mathematics	36.4	7
solving-quantitative-reasoning-problems-with	78.5	540
llemma-an-open-language-model-for-mathematics	51.5	34
metamath-bootstrap-your-own-mathematical	71.0	13
mathcoder-seamless-code-integration-in-llms	83.9	70
query-and-response-augmentation-cannot-help	74	13
the-unreasonable-effectiveness-of-eccentric	61	70
solving-quantitative-reasoning-problems-with	33.0	62
the-claude-3-model-family-opus-sonnet-haiku	95	-
toward-self-improvement-of-llms-via	92	70
mistral-7b	52.2	7
llama-open-and-efficient-foundation-language-1	29.3	13
unifying-language-learning-paradigms	4.1	20
learning-from-self-sampled-correct-and	7.5	0.125
step-dpo-step-wise-preference-optimization	94.0	-
النموذج 136	74.7	7
openmathinstruct-1-a-1-8-million-math	86.9	7
composing-ensembles-of-pre-trained-models-via	20.8	0.355
breaking-the-ceiling-of-the-llm-community-by	90.91	-
tinygsm-achieving-80-on-gsm8k-with-small	74.3	2.7
solving-quantitative-reasoning-problems-with	68.5	62
tora-a-tool-integrated-reasoning-agent-for	88.3	70
النموذج 143	72.3	-
wizardmath-empowering-mathematical-reasoning	54.9	7
llama-open-and-efficient-foundation-language-1	69.7	65
dart-math-difficulty-aware-rejection-tuning-1	81.1	8
mathcoder-seamless-code-integration-in-llms	81.7	34
solving-math-word-problem-via-cooperative	63.2	12
النموذج 149	85.5	-
boosting-llm-reasoning-push-the-limits-of-few	59.59	70
parameter-efficient-sparsity-crafting-from	77.8	-
branch-train-mix-mixing-expert-llms-into-a	37.1	-
openmathinstruct-1-a-1-8-million-math	88.0	34
an-empirical-study-of-data-ability-boundary	87.2	7
mathcoder-seamless-code-integration-in-llms	64.2	7
tora-a-tool-integrated-reasoning-agent-for	85.1	34
النموذج 157	87.41	4
dart-math-difficulty-aware-rejection-tuning-1	89.6	70
dart-math-difficulty-aware-rejection-tuning-1	86.8	7
outcome-supervised-verifiers-for-planning-in	82.6	7