content.py

TITLE = '<h1 align="center" id="space-title">Open Dutch LLM Evaluation Leaderboard</h1>'

INTRO_TEXT = f"""
## About

This is a leaderboard for Dutch benchmarks for large language models.

This is a fork of the [Open Multilingual LLM Evaluation Leaderboard](https://huggingface.co/spaces/uonlp/open_multilingual_llm_leaderboard), but restricted to only Dutch models and augmented with additional model results.
We test the models on the following benchmarks **for the Dutch version only!!**, which have been translated into Dutch automatically by the original authors of the Open Multilingual LLM Evaluation Leaderboard with `gpt-35-turbo`.

- <a href="https://arxiv.org/abs/1803.05457" target="_blank">  AI2 Reasoning Challenge </a> (25-shot) 
- <a href="https://arxiv.org/abs/1905.07830" target="_blank">  HellaSwag </a> (10-shot) 
- <a href="https://arxiv.org/abs/2009.03300" target="_blank">  MMLU </a>  (5-shot) 
- <a href="https://arxiv.org/abs/2109.07958" target="_blank">  TruthfulQA </a> (0-shot)

I do not maintain those datasets, I only run benchmarks and add the results to this space. For questions regarding the test sets or running them yourself, see [the original Github repository](https://github.com/laiviet/lm-evaluation-harness).

**Disclaimer**: I am aware that benchmarking models on *translated* data is not ideal. However, for Dutch there are no other options for generative models at the moment. Because the benchmarks were automatically translated, some translationese effects may occur: the translations may not be fluent Dutch or still contain artifacts of the source text (like word order, literal translation, certain vocabulary items). Because of that, an unfair advantage may be given to the non-Dutch models: Dutch is closely related to English, so if the benchmarks are in automatically translated Dutch that still has English properties, those English models may not have too many issues with that. If the benchmarks were to have been manually translated or, even better, created from scratch in Dutch, those non-Dutch models may have a harder time. Maybe not. We cannot know for sure until we have high-quality, manually crafted benchmarks for Dutch.
 
If you have any suggestions for other Dutch benchmarks, please let me know so I can add them!
"""

CREDIT = f"""
## Credit

This leaderboard has borrowed heavily from the following sources:

- Datasets (AI2_ARC, HellaSwag, MMLU, TruthfulQA)
- Evaluation code (EleutherAI's lm_evaluation_harness repo)
- Leaderboard code (Huggingface4's open_llm_leaderboard repo)
- The multilingual version of the leaderboard (uonlp's open_multilingual_llm_leaderboard repo)

"""


CITATION = f"""
## Citation


If you use or cite the Dutch benchmark results or this specific leaderboard page, please cite the following paper:

TBA


If you use the multilingual benchmarks, please cite the following paper:

```bibtex
@misc{{lai2023openllmbenchmark,
    author = {{Viet Lai and Nghia Trung Ngo and Amir Pouran Ben Veyseh and Franck Dernoncourt and Thien Huu Nguyen}},
    title={{Open Multilingual LLM Evaluation Leaderboard}},
    year={{2023}}
}}
```
"""