rank
int64 1
112
| model
stringlengths 5
65
| accuracy
float64 10.6
89.7
| parameters
float64 1.5
540
⌀ | extra_training_data
stringclasses 2
values | paper
stringlengths 0
110
| code
stringclasses 3
values | result
stringclasses 3
values | year
int64 2.02k
2.02k
| tags
listlengths 0
3
|
|---|---|---|---|---|---|---|---|---|---|
101
|
MetaMath 13B
| 22.5
| 13
|
Yes
|
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
|
Yes
|
No
| 2,023
|
[
"fine-tuned"
] |
102
|
davinci-002 175B
| 19.1
| 175
|
No
|
Solving Quantitative Reasoning Problems with Language Models
|
Yes
|
No
| 2,022
|
[] |
103
|
Branch-Train-MiX 4x7B (sampling top-2 experts)
| 17.8
| null |
No
|
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
|
Yes
|
No
| 2,024
|
[] |
104
|
GAL 120B (5-shot)
| 16.6
| 120
|
No
|
Galactica: A Large Language Model for Science
|
Yes
|
No
| 2,022
|
[] |
105
|
LLaMA 33B-maj1@k
| 15.2
| 33
|
No
|
LLaMA: Open and Efficient Foundation Language Models
|
Yes
|
No
| 2,023
|
[
"majority voting"
] |
106
|
Minerva 8B
| 14.1
| 8
|
No
|
Solving Quantitative Reasoning Problems with Language Models
|
Yes
|
No
| 2,022
|
[] |
107
|
WizardMath-13B-V1.0
| 14
| 13
|
Yes
|
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
|
Yes
|
No
| 2,023
|
[] |
108
|
LLaMA 65B
| 10.6
| 65
|
No
|
LLaMA: Open and Efficient Foundation Language Models
|
Yes
|
No
| 2,023
|
[] |
109
|
GAL 30B (5-shot)
| 12.7
| 30
|
No
|
Galactica: A Large Language Model for Science
|
Yes
|
No
| 2,022
|
[] |
110
|
Mistral 7B (maj@4)
| 13.1
| 7
|
No
|
Mistral 7B
|
Yes
|
No
| 2,023
|
[] |
111
|
GAL 30B <work>
| 11.4
| 30
|
No
|
Galactica: A Large Language Model for Science
|
Yes
|
No
| 2,022
|
[] |
112
|
WizardMath-7B-V1.0
| 10.7
| 7
|
Yes
|
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
|
Yes
|
No
| 2,023
|
[] |
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