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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