content.py

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

INTRO_TEXT = f"""## About

**Note**: this page is no longer maintained. Insteader, refer to [ScandEval benchmarks for Dutch](https://scandeval.com/dutch-nlg/).

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`.
I did not verify their translations and I do not maintain the datasets, I only run the 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).

<p align="center">
  <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)
</p>
"""

DISCLAIMER = """## Disclaimer

**Evaluating generative models.** Counter-intuitively, we often evaluate generative models with multiple choice questions (as done here). This is useful to gauge the reasoning capabilities of LLMs. However, they do not account for the user experience, including how fluent and natural the text is. A prime example is how top models such as Zephyr, Mistral and Mixtral are actually quite poor when using them as a chatbot for Dutch. But they appear to be good at at least "understanding" a task in Dutch and correctly reasoning about it. Similarly, for humans understanding the general gist of a (new) written language (like after a few months on Duolingo) is something completely different from writing an eloquent, native-level article. This is an under-researched part of evaluating LLMs, especially in non-English languages.

**Translations of benchmarks.** I did not verify the (translation) quality of the benchmarks. If you encounter issues with the benchmark contents, please contact the original authors.

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.

Another shortcoming is that we do not calculate significancy scores or confidence intervals. When results are close together in the leaderboard I therefore urge caution when interpreting the model ranks.

If you have any suggestions for other Dutch benchmarks, please [let me know](https://twitter.com/BramVanroy) 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 = """## Citation


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

Vanroy, B. (2023). *Language Resources for Dutch Large Language Modelling*. [https://arxiv.org/abs/2312.12852](https://arxiv.org/abs/2312.12852)

```bibtext
@article{vanroy2023language,
  title={Language Resources for {Dutch} Large Language Modelling},
  author={Vanroy, Bram},
  journal={arXiv preprint arXiv:2312.12852},
  year={2023}
}
```


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

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