Measuring Massive Multitask Language Understanding

This is the repository for Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).

This repository contains OpenAI API evaluation code, and the test is available for download here.

Test Leaderboard

If you want to have your model added to the leaderboard, please reach out to us or submit a pull request.

Results of the test:

Model	Authors	Humanities	Social Sciences	STEM	Other	Average
Chinchilla (70B, few-shot)	Hoffmann et al., 2022	63.6	79.3	54.9	73.9	67.5
Gopher (280B, few-shot)	Rae et al., 2021	56.2	71.9	47.4	66.1	60.0
GPT-3 (175B, fine-tuned)	Brown et al., 2020	52.5	63.9	41.4	57.9	53.9
flan-T5-xl	Chung et al., 2022	46.3	57.7	39.0	55.1	49.3
UnifiedQA	Khashabi et al., 2020	45.6	56.6	40.2	54.6	48.9
GPT-3 (175B, few-shot)	Brown et al., 2020	40.8	50.4	36.7	48.8	43.9
GPT-3 (6.7B, fine-tuned)	Brown et al., 2020	42.1	49.2	35.1	46.9	43.2
flan-T5-large	Chung et al., 2022	39.1	49.1	33.2	47.4	41.9
flan-T5-base	Chung et al., 2022	34.0	38.1	27.6	37.0	34.2
GPT-2	Radford et al., 2019	32.8	33.3	30.2	33.1	32.4
flan-T5-small	Chung et al., 2022	29.9	30.9	27.5	29.7	29.5
Random Baseline	N/A	25.0	25.0	25.0	25.0	25.0

Citation

If you find this useful in your research, please consider citing the test and also the ETHICS dataset it draws from:

@article{hendryckstest2021,
  title={Measuring Massive Multitask Language Understanding},
  author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2021}
}

@article{hendrycks2021ethics,
  title={Aligning AI With Shared Human Values},
  author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2021}
}

Test Leaderboard

If you want to have your model added to the leaderboard, please reach out to us or submit a pull request.

Results of the test:

Model

Authors

Humanities

Social Sciences

STEM

Other

Average

Chinchilla (70B, few-shot)

Hoffmann et al., 2022

63.6

79.3

54.9

73.9

67.5

Gopher (280B, few-shot)

Rae et al., 2021

56.2

71.9

47.4

66.1

60.0

GPT-3 (175B, fine-tuned)

Brown et al., 2020

52.5

63.9

41.4

57.9

53.9

flan-T5-xl

Chung et al., 2022

46.3

57.7

39.0

55.1

49.3

UnifiedQA

Khashabi et al., 2020

45.6

56.6

40.2

54.6

48.9

GPT-3 (175B, few-shot)

Brown et al., 2020

40.8

50.4

36.7

48.8

43.9

GPT-3 (6.7B, fine-tuned)

Brown et al., 2020

42.1

49.2

35.1

46.9

43.2

flan-T5-large

Chung et al., 2022

39.1

49.1

33.2

47.4

41.9

flan-T5-base

Chung et al., 2022

34.0

38.1

27.6

37.0

34.2

GPT-2

Radford et al., 2019

32.8

33.3

30.2

33.1

32.4

flan-T5-small

Chung et al., 2022

29.9

30.9

27.5

29.7

29.5

Random Baseline

N/A

25.0

Citation

If you find this useful in your research, please consider citing the test and also the ETHICS dataset it draws from:

@article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} }