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Valentin Buchner
huggingface leaderboard and github action to update markdown leaderboard from json
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TITLE = """<h1 align="center" id="space-title">🔥🏅️GenCeption Leaderboard 🏅️🔥</h1>""" | |
BANNER = """<div> | |
<p align="center"> | |
<a href="https://github.com/EQTPartners/GenCeption/">GitHub</a> •  | |
<a href="https://github.com/EQTPartners/GenCeption#contribute">Contribute</a> •  | |
<a href="https://arxiv.org/abs/2402.14973">Paper</a> •  | |
<a href="https://github.com/EQTPartners/GenCeption#cite-this-work">Citation</a> | |
</p> | |
""" | |
INTRO = """GenCeption is an annotation-free MLLM (Multimodal Large Language Model) evaluation framework that merely requires unimodal data to assess inter-modality semantic coherence and inversely reflects the models' inclination to hallucinate.""" | |
INTRO2 = """This leaderboard displays the evaluated models ranked by their performance on the **GC@3** metric, as defined in [GenCeption: Evaluate Multimodal LLMs with Unlabeled Unimodal Data](https://arxiv.org/abs/2402.14973). For contributing a model evaluation, please submit a pull request on [GitHub](https://github.com/EQTPartners/GenCeption).""" | |
CITATION_BUTTON_LABEL = "Copy the following snippet to cite this benchmark" | |
CITATION_BUTTON_TEXT = r""" | |
@article{cao2023genception, | |
author = {Lele Cao and | |
Valentin Buchner and | |
Zineb Senane and | |
Fangkai Yang}, | |
title = {{GenCeption}: Evaluate Multimodal LLMs with Unlabeled Unimodal Data}, | |
year={2023}, | |
journal={arXiv preprint arXiv:2402.14973}, | |
primaryClass={cs.AI,cs.CL,cs.LG} | |
} | |
""" | |