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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