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    <abstract>This paper introduces Evalverse, a novel library that streamlines the evaluation of Large Language Models (LLMs) by unifying disparate evaluation tools into a single, user-friendly framework. Evalverse enables individuals with limited knowledge of artificial intelligence to easily request LLM evaluations and receive detailed reports, facilitated by an integration with communication platforms like Slack. Thus, Evalverse serves as a powerful tool for the comprehensive assessment of LLMs, offering both researchers and practitioners a centralized and easily accessible evaluation framework. Finally, we also provide a demo video for Evalverse, showcasing its capabilities and implementation in a two-minute format.</abstract>
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%0 Conference Proceedings
%T Evalverse: Unified and Accessible Library for Large Language Model Evaluation
%A Kim, Jihoo
%A Song, Wonho
%A Kim, Dahyun
%A Kim, Yunsu
%A Kim, Yungi
%A Park, Chanjun
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kim-etal-2024-evalverse
%X This paper introduces Evalverse, a novel library that streamlines the evaluation of Large Language Models (LLMs) by unifying disparate evaluation tools into a single, user-friendly framework. Evalverse enables individuals with limited knowledge of artificial intelligence to easily request LLM evaluations and receive detailed reports, facilitated by an integration with communication platforms like Slack. Thus, Evalverse serves as a powerful tool for the comprehensive assessment of LLMs, offering both researchers and practitioners a centralized and easily accessible evaluation framework. Finally, we also provide a demo video for Evalverse, showcasing its capabilities and implementation in a two-minute format.
%U https://aclanthology.org/2024.emnlp-demo.3
%P 25-33

Markdown (Informal)

[Evalverse: Unified and Accessible Library for Large Language Model Evaluation](https://aclanthology.org/2024.emnlp-demo.3) (Kim et al., EMNLP 2024)

• Evalverse: Unified and Accessible Library for Large Language Model Evaluation (Kim et al., EMNLP 2024)

ACL

• Jihoo Kim, Wonho Song, Dahyun Kim, Yunsu Kim, Yungi Kim, and Chanjun Park. 2024. Evalverse: Unified and Accessible Library for Large Language Model Evaluation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 25–33, Miami, Florida, USA. Association for Computational Linguistics.