README.md

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# SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models


## Citation
```bib
[Citation placeholder]
```

## Groups

- `score_robustness_mmlu_pro`: two 0-shot robutstness tasks on MMLU-PRO dataset [[1](#mmlu_pro)]

- `score_robustness_agieval`: two 0-shot robutstness tasks on the AGIEVAL datasets [[2](#agi_eval)] multiple choice questions subsets:  `'agieval-sat-math'`, `'agieval-lsat-lr'`, `'agieval-lsat-rc'`, `'agieval-logiqa-en'`, `'agieval-aqua-rat'`, `'agieval-sat-en'`, `'agieval-lsat-ar'`

- `score_robustness_math`: one 0-shot robutstness tasks on Hendryk's MATH dataset [[3](#math)]

## Tasks

Both `score_robustness_mmlu_pro` and `score_robustness_agieval` contain the following 3 tasks:

* Option order robustness:
`score_option_order_robustness_mmlu_pro`,
`score_option_order_robustness_agieval`

* Prompt robustness:
`score_prompt_robustness_mmlu_pro`,
`score_prompt_robustness_agieval`,

* Non greedy robustness
`score_non_greedy_robustness_mmlu_pro`,
`score_non_greedy_robustness_agieval`,

Whereas math contains the following 2:
* Prompt robustness:
`score_prompt_robustness_math`
`score_non_greedy_robustness_math`,

### Option order robustness

Measures the model's robustness to the placement of the correct answer in the options list by swapping the correct answer with all the other possible options.

### Prompt robustness

Measures the model's robustness to 10 different prompts. list of the prompts can be found in the `./prompt_templates.json` file under the key `prompt_robustness`.


### Non greedy robustness

Measures the model's robustness to 5 different seeds: seeds = \[1-5\]. For evaluating on the non greedy task, please, refer to [NON_GREEDY.md](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/score/NON_GREEDY.md)

## Metrics

All robustness tasks calculate 2 metrics: *Accuracy* and *Consistency Rate(CR)* [[4](#cr)].

$CR = \frac{1}{|Q|} \sum_{Q_k \in Q} \sum_{y_i \in Y_k} \sum_{\substack{y_j \in Y_k \\ j \neq i}}\frac{\text{sim}(y_i, y_j)}{\binom{|Y_k|}{2}}$

## Notes

- All tasks are designed for **Instruct** models for which we recommend to pass "`--apply_chat_template`" flag.


## References
<a name=mmlu_pro></a>[1] Wang, et al. "Mmlu-pro: A more robust and challenging multi-task language understanding benchmark." arXiv preprint arXiv:2406.01574 (2024).

<a name=agi_eval></a>[2] Zhong, et al. "Agieval: A human-centric benchmark for evaluating foundation models." arXiv preprint arXiv:2304.06364 (2023).

<a name=math></a>[3] Hendrycks et al. "Measuring Mathematical Problem Solving With the MATH Dataset." arXiv:2103.03874 (2021).

<a name=cr></a>[4] Yukun et al. "Improving the robustness of large language models via consistency alignment." arXiv:2403.14221 (2024).

## Checklist

For adding novel benchmarks/datasets to the library:
* [-] Is the task an existing benchmark in the literature?
  * [-] Have you referenced the original paper that introduced the task? - Will be referenced as soon as the paper is published
  * [ ] 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?