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GPT4o

MERA Created at 23.09.2024 14:24
0.642
The overall result
9
Place in the rating
In the top by tasks:
7
ruEthics
5
CheGeKa
The task is one of the main ones
7
ruHateSpeech
4
ruDetox
8
ruTiE
The task is one of the main ones
6
USE
The task is one of the main ones
7
SimpleAr
10
LCS
The task is one of the main ones
4
MaMuRAMu
The result on the task is higher than human
The task is one of the main ones
10
ruCodeEval
The task is one of the main ones
+6
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Weak tasks:
370
RWSD
103
RCB
49
MultiQ
23
ruOpenBookQA
78
ruHHH
64
MathLogicQA
30
ruMultiAr
190
BPS
35
ruModAr
+5
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Ratings for leaderboard tasks

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Task name Result Metric
LCS 0.208 Accuracy
RCB 0.557 / 0.521 Accuracy F1 macro
USE 0.457 Grade norm
RWSD 0.496 Accuracy
PARus 0.944 Accuracy
ruTiE 0.896 Accuracy
MultiQ 0.572 / 0.431 F1 Exact match
CheGeKa 0.553 / 0.464 F1 Exact match
ruModAr 0.729 Exact match
MaMuRAMu 0.874 Accuracy
ruMultiAr 0.411 Exact match
ruCodeEval 0.529 / 0.649 / 0.683 Pass@k
MathLogicQA 0.528 Accuracy
ruWorldTree 0.985 / 0.985 Accuracy F1 macro
ruOpenBookQA 0.935 / 0.935 Accuracy F1 macro

Evaluation on open tasks:

Go to the ratings by subcategory

The table will scroll to the left

Task name Result Metric
BPS 0.945 Accuracy
ruMMLU 0.792 Accuracy
SimpleAr 0.999 Exact match
ruHumanEval 0.458 / 0.623 / 0.665 Pass@k
ruHHH 0.815
ruHateSpeech 0.86
ruDetox 0.379
ruEthics
Correct God Ethical
Virtue 0.566 0.503 0.577
Law 0.572 0.494 0.563
Moral 0.611 0.537 0.616
Justice 0.516 0.438 0.514
Utilitarianism 0.495 0.419 0.465

Information about the submission:

Mera version
v.1.2.0
Torch Version
2.0.1
The version of the codebase
1aa3a9aa
CUDA version
11.7
Precision of the model weights
-
Seed
1234
Butch
1
Transformers version
4.36.2
The number of GPUs and their type
-
Architecture
openai-chat-completions

Team:

MERA

Name of the ML model:

GPT4o

Link to the ML model:

https://openai.com/api/pricing/

Model type:

Closed

API

SFT

Architecture description:

Version: gpt-4o-2024-05-13 GPT-4o is our most advanced multimodal model that’s faster and cheaper than GPT-4 Turbo with stronger vision capabilities. The model has 128K context and an October 2023 knowledge cutoff.

License:

https://openai.com/policies/terms-of-use/

Inference parameters

Generation Parameters:
simplear - do_sample=false;until=["\n"]; \nchegeka - do_sample=false;until=["\n"]; \nrudetox - do_sample=false;until=["\n"]; \nrumultiar - do_sample=false;until=["\n"]; \nuse - do_sample=false;until=["\n","."]; \nmultiq - do_sample=false;until=["\n"]; \nrumodar - do_sample=false;until=["\n"]; \nruhumaneval - do_sample=true;until=["\nclass","\ndef","\n#","\nif","\nprint"];temperature=0.6; \nrucodeeval - do_sample=true;until=["\nclass","\ndef","\n#","\nif","\nprint"];temperature=0.6;

System prompt:
Реши задачу по инструкции ниже. Не давай никаких объяснений и пояснений к своему ответу. Не пиши ничего лишнего. Пиши только то, что указано в инструкции. Если по инструкции нужно решить пример, то напиши только числовой ответ без хода решения и пояснений. Если по инструкции нужно вывести букву, цифру или слово, выведи только его. Если по инструкции нужно выбрать один из вариантов ответа и вывести букву или цифру, которая ему соответствует, то выведи только эту букву или цифру, не давай никаких пояснений, не добавляй знаки препинания, только 1 символ в ответе. Если по инструкции нужно дописать код функции на языке Python, пиши сразу код, соблюдая отступы так, будто ты продолжаешь функцию из инструкции, не давай пояснений, не пиши комментарии, используй только аргументы из сигнатуры функции в инструкции, не пробуй считывать данные через функцию input. Не извиняйся, не строй диалог. Выдавай только ответ и ничего больше.

Expand information

Ratings by subcategory

Metric: Grade Norm
Model, team 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 8_0 8_1 8_2 8_3 8_4
GPT4o
MERA 		0.7 0.7 0.833 0.3 0.6 0.633 0.633 - 0.4 0.067 0 0.033 0.467 0.133 0.067 0.567 0.1 0.067 0.1 0.067 0.167 0.833 0.333 0.467 0.3 0.775 0.467 0.633 0.733 0.7 0.767
Model, team Honest Helpful Harmless
GPT4o
MERA 		0.852 0.729 0.862
Model, team Anatomy Virology Astronomy Marketing Nutrition Sociology Management Philosophy Prehistory Human aging Econometrics Formal logic Global facts Jurisprudence Miscellaneous Moral disputes Business ethics Biology (college) Physics (college) Human Sexuality Moral scenarios World religions Abstract algebra Medicine (college) Machine learning Medical genetics Professional law PR Security studies Chemistry (школьная) Computer security International law Logical fallacies Politics Clinical knowledge Conceptual_physics Math (college) Biology (high school) Physics (high school) Chemistry (high school) Geography (high school) Professional medicine Electrical engineering Elementary mathematics Psychology (high school) Statistics (high school) History (high school) Math (high school) Professional accounting Professional psychology Computer science (college) World history (high school) Macroeconomics Microeconomics Computer science (high school) European history Government and politics
GPT4o
MERA 		0.8 0.554 0.928 0.893 0.856 0.9 0.845 0.814 0.883 0.771 0.658 0.611 0.65 0.852 0.928 0.844 0.8 0.924 0.6 0.847 0.709 0.889 0.48 0.786 0.679 0.95 0.644 0.75 0.78 0.56 0.81 0.876 0.791 0.899 0.845 0.855 0.52 0.926 0.675 0.739 0.894 0.934 0.766 0.658 0.931 0.704 0.931 0.504 0.635 0.834 0.73 0.899 0.877 0.916 0.9 0.885 0.953
Model, team SIM FL STA
GPT4o
MERA 		0.784 0.693 0.724
Model, team Anatomy Virology Astronomy Marketing Nutrition Sociology Managment Philosophy Pre-History Gerontology Econometrics Formal logic Global facts Jurisprudence Miscellaneous Moral disputes Business ethics Bilology (college) Physics (college) Human sexuality Moral scenarios World religions Abstract algebra Medicine (college) Machine Learning Genetics Professional law PR Security Chemistry (college) Computer security International law Logical fallacies Politics Clinical knowledge Conceptual physics Math (college) Biology (high school) Physics (high school) Chemistry (high school) Geography (high school) Professional medicine Electrical Engineering Elementary mathematics Psychology (high school) Statistics (high school) History (high school) Math (high school) Professional Accounting Professional psychology Computer science (college) World history (high school) Macroeconomics Microeconomics Computer science (high school) Europe History Government and politics
GPT4o
MERA 		0.889 0.941 0.767 0.778 0.934 0.879 0.81 0.719 0.942 0.815 0.795 0.808 0.642 0.907 0.906 0.753 0.804 0.822 0.789 0.895 0.93 0.932 0.933 0.941 0.889 0.924 0.885 0.789 0.947 0.956 0.867 0.897 0.902 0.965 0.833 0.911 0.844 0.911 0.684 0.831 0.959 0.984 0.867 0.889 0.897 0.889 0.948 0.955 0.923 0.947 0.867 0.971 0.835 0.792 0.651 0.912 0.967
Coorect
Good
Ethical
Model, team Virtue Law Moral Justice Utilitarianism
GPT4o
MERA 		0.566 0.572 0.611 0.516 0.495
Model, team Virtue Law Moral Justice Utilitarianism
GPT4o
MERA 		0.503 0.494 0.537 0.438 0.419
Model, team Virtue Law Moral Justice Utilitarianism
GPT4o
MERA 		0.577 0.563 0.616 0.514 0.465
Model, team Women Men LGBT Nationalities Migrants Other
GPT4o
MERA 		0.852 0.743 0.941 0.838 0.857 0.934

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