Accuracy on MMLU's marketing questions, one of 57 subject tests of academic and professional knowledge.
unassessed
| Category | knowledge |
|---|---|
| Subcategory | Other |
| Page status | active |
| Metric | accuracy |
| Direction | higher_is_better |
| Unit | % |
| Dataset size | 234 |
| Dataset licence | MIT |
| Publisher | UC Berkeley |
Marketing principles at the level of an introductory business course: the marketing mix, branding, market segmentation, consumer behaviour, and promotion strategy. Framed as four-option multiple-choice questions and scored zero-shot or few-shot by exact match against the labelled option, as one of the 57 subject subsets that make up the MMLU benchmark.
Four-option multiple-choice question answering (A-D), one correct answer, zero-shot or few-shot.
| Model | Provider | Score | Card as of |
|---|---|---|---|
| Yi 1.5 34B 32K | 01.AI | 95.3 | 2024-07 |
| Yi 1.5 34B | 01.AI | 94.9 | 2026-04 |
| Yi 34B 200K | 01.AI | 94.0 | 2024-07 |
| Meta Llama 3 70B | Meta | 93.6 | 2024-07 |
| Meta Llama 3 70B Instruct | Meta | 93.6 | 2026-04 |
| Meta Llama 3 70B Instruct | Nous Research | 93.6 | 2026-04 |
| Mixtral 8x22B Instruct v0.1 | Mistral AI | 92.7 | 2026-04 |
| Mixtral 8x7B Instruct v0.1 | Mistral AI | 92.3 | 2026-04 |
| Yi 1.5 34B Chat | 01.AI | 92.3 | 2026-04 |
| Yi 1.5 34B Chat 16K | 01.AI | 92.3 | 2024-07 |
| Mixtral 8x7B v0.1 | Mistral AI | 91.9 | 2026-04 |
| Nous Hermes 2 Yi 34B | Nous Research | 91.9 | 2024-07 |
| Yi 1.5 9B Chat | 01.AI | 91.9 | 2024-07 |
| Yi 1.5 9B Chat 16K | 01.AI | 91.5 | 2024-07 |
| Yi 34B Chat | 01.AI | 91.5 | 2024-07 |
| Yi 9B | 01.AI | 91.0 | 2024-07 |
| Meta Llama 3 8B Instruct | Meta | 90.6 | 2024-07 |
| Meta Llama 3 8B Instruct | Nous Research | 90.6 | 2024-07 |
| Nous Hermes 2 Mixtral 8x7B DPO | Nous Research | 90.2 | 2024-07 |
| Phi 3 mini 4K instruct | Microsoft | 90.2 | 2024-07 |
| Yi 1.5 6B | 01.AI | 90.2 | 2024-07 |
| Yi 6B | 01.AI | 90.2 | 2024-07 |
| Yi 6B Chat | 01.AI | 90.2 | 2024-07 |
| gemma 7B it | Google DeepMind | 89.7 | 2024-07 |
| Meta Llama 3 8B | Meta | 89.7 | 2024-07 |
| Meta Llama 3 8B | Nous Research | 89.7 | 2024-07 |
| Yi 1.5 9B | 01.AI | 89.7 | 2024-07 |
| Yi 1.5 9B 32K | 01.AI | 89.7 | 2024-07 |
| Hermes 2 Theta Llama 3 8B | Nous Research | 89.3 | 2024-07 |
| Phi 3 mini 128K instruct | Microsoft | 88.9 | 2024-07 |
| Yi 1.5 6B Chat | 01.AI | 88.9 | 2024-07 |
| Mistral 7B v0.3 | Mistral AI | 88.0 | 2024-07 |
| mistral 7B v0.3 bnb 4bit | Unsloth | 88.0 | 2024-07 |
| Nous Hermes 2 SOLAR 10.7B | Nous Research | 88.0 | 2024-07 |
| Hermes 2 Pro Llama 3 8B | Nous Research | 87.6 | 2024-07 |
| Mistral 7B Instruct v0.2 | Mistral AI | 86.3 | 2024-07 |
| phi 2 | Microsoft | 82.5 | 2024-07 |
| deepseek llm 7B base | DeepSeek | 82.1 | 2024-07 |
| deepseek llm 7B chat | DeepSeek | 82.1 | 2024-07 |
| Qwen2 1.5B Instruct | Alibaba / Qwen Team | 80.3 | 2024-07 |
| falcon 40B | TII | 79.1 | 2024-07 |
| chatglm2 6B | Zhipu AI | 69.2 | 2024-07 |
| Qwen2 0.5B Instruct | Alibaba / Qwen Team | 68.4 | 2024-07 |
| deepseek coder 6.7B instruct | DeepSeek | 65.4 | 2024-07 |
| deepseek coder 6.7B base | DeepSeek | 63.7 | 2024-07 |
| gemma 2B | Google DeepMind | 60.7 | 2024-07 |
| gemma 2B it | Google DeepMind | 59.4 | 2024-07 |
| deepseek coder 1.3B base | DeepSeek | 33.3 | 2024-07 |
| deepseek coder 1.3B instruct | DeepSeek | 33.3 | 2024-07 |
| OLMo 1B hf | Allen AI | 25.2 | 2024-07 |