The ETHICS dataset is a benchmark that spans concepts in justice, well-being,
duties, virtues, and commonsense morality. Models predict widespread moral
judgments about diverse text scenarios. This requires connecting physical and
social world knowledge to value judgements, a capability that may enable us
to steer chatbot outputs or eventually regularize open-ended reinforcement
learning agents.
Homepage: https://github.com/hendrycks/ethics
### Citation
```
@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}
}
```
### Subtasks
*`ethics_cm`:
*
Missing:
*`ethics_utilitarianism_original`:
### Checklist
* [x] Is the task an existing benchmark in the literature?
* [ ] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [x] Is the "Main" variant of this task clearly denoted?
* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
https://arxiv.org/pdf/1905.13319.pdf
MathQA is a large-scale dataset of 37k English multiple-choice math word problems
covering multiple math domain categories by modeling operation programs corresponding
to word problems in the AQuA dataset (Ling et al., 2017).
Homepage: https://math-qa.github.io/math-QA/
### Citation
```
@misc{amini2019mathqa,
title={MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms},
author={Aida Amini and Saadia Gabriel and Peter Lin and Rik Koncel-Kedziorski and Yejin Choi and Hannaneh Hajishirzi},
year={2019},
eprint={1905.13319},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Subtasks
*`mathqa`: The MathQA dataset, as a multiple choice dataset where the answer choices are not in context.
### Checklist
For adding novel benchmarks/datasets to the library:
* [x] Is the task an existing benchmark in the literature?
* [x] Have you referenced the original paper that introduced the task?
* [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
* The MathQA dataset predates transformer-based prompted LLMs. We should, however, return to this task to ensure equivalence to the non-CoT version of mathQA used in the Chain-of-Thought paper.
If other tasks on this dataset are already supported:
* [x] Is the "Main" variant of this task clearly denoted?
* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [x] Have you noted which, if any, published evaluation setups are matched by this variant?
* [x] Checked for equivalence with v0.3.0 LM Evaluation Harness
author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
*`anagrams1` - Anagrams of all but the first and last letter.
*`anagrams2` - Anagrams of all but the first and last 2 letters.
*`cycle_letters` - Cycle letters in a word.
*`random_insertion` - Random insertions in the word that must be removed.
*`reversed_words` - Words spelled backwards that must be reversed.
### Checklist
For adding novel benchmarks/datasets to the library:
* [x] Is the task an existing benchmark in the literature?
* [x] Have you referenced the original paper that introduced the task?
* [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [x] Is the "Main" variant of this task clearly denoted?
* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [x] Have you noted which, if any, published evaluation setups are matched by this variant?
* [x] Checked for equivalence with v0.3.0 LM Evaluation Harness
