@inproceedings{he-etal-2024-ultraeval,
title = "{U}ltra{E}val: A Lightweight Platform for Flexible and Comprehensive Evaluation for {LLM}s",
author = "He, Chaoqun and
Luo, Renjie and
Hu, Shengding and
Zhao, Ranchi and
Zhou, Jie and
Wu, Hanghao and
Zhang, Jiajie and
Han, Xu and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Cao, Yixin and
Feng, Yang and
Xiong, Deyi",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-demos.23",
doi = "10.18653/v1/2024.acl-demos.23",
pages = "247--257",
abstract = "Evaluation is pivotal for honing Large Language Models (LLMs), pinpointing their capabilities and guiding enhancements. The rapid development of LLMs calls for a lightweight and easy-to-use framework for swift evaluation deployment. However, due to the various implementation details to consider, developing a comprehensive evaluation platform is never easy. Existing platforms are often complex and poorly modularized, hindering seamless incorporation into researcher{'}s workflows. This paper introduces UltraEval, a user-friendly evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency. We identify and reimplement three core components of model evaluation (models, data, and metrics). The resulting composability allows for the free combination of different models, tasks, prompts, and metrics within a unified evaluation workflow. Additionally, UltraEval supports diverse models owing to a unified HTTP service and provides sufficient inference acceleration.",
}
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<abstract>Evaluation is pivotal for honing Large Language Models (LLMs), pinpointing their capabilities and guiding enhancements. The rapid development of LLMs calls for a lightweight and easy-to-use framework for swift evaluation deployment. However, due to the various implementation details to consider, developing a comprehensive evaluation platform is never easy. Existing platforms are often complex and poorly modularized, hindering seamless incorporation into researcher’s workflows. This paper introduces UltraEval, a user-friendly evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency. We identify and reimplement three core components of model evaluation (models, data, and metrics). The resulting composability allows for the free combination of different models, tasks, prompts, and metrics within a unified evaluation workflow. Additionally, UltraEval supports diverse models owing to a unified HTTP service and provides sufficient inference acceleration.</abstract>
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%0 Conference Proceedings
%T UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs
%A He, Chaoqun
%A Luo, Renjie
%A Hu, Shengding
%A Zhao, Ranchi
%A Zhou, Jie
%A Wu, Hanghao
%A Zhang, Jiajie
%A Han, Xu
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Cao, Yixin
%Y Feng, Yang
%Y Xiong, Deyi
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F he-etal-2024-ultraeval
%X Evaluation is pivotal for honing Large Language Models (LLMs), pinpointing their capabilities and guiding enhancements. The rapid development of LLMs calls for a lightweight and easy-to-use framework for swift evaluation deployment. However, due to the various implementation details to consider, developing a comprehensive evaluation platform is never easy. Existing platforms are often complex and poorly modularized, hindering seamless incorporation into researcher’s workflows. This paper introduces UltraEval, a user-friendly evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency. We identify and reimplement three core components of model evaluation (models, data, and metrics). The resulting composability allows for the free combination of different models, tasks, prompts, and metrics within a unified evaluation workflow. Additionally, UltraEval supports diverse models owing to a unified HTTP service and provides sufficient inference acceleration.
%R 10.18653/v1/2024.acl-demos.23
%U https://aclanthology.org/2024.acl-demos.23
%U https://doi.org/10.18653/v1/2024.acl-demos.23
%P 247-257
Markdown (Informal)
[UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs](https://aclanthology.org/2024.acl-demos.23) (He et al., ACL 2024)
ACL
- Chaoqun He, Renjie Luo, Shengding Hu, Ranchi Zhao, Jie Zhou, Hanghao Wu, Jiajie Zhang, Xu Han, Zhiyuan Liu, and Maosong Sun. 2024. UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 247–257, Bangkok, Thailand. Association for Computational Linguistics.