Evalverse: Unified and Accessible Library for Large Language Model Evaluation

Jihoo Kim, Wonho Song, Dahyun Kim, Yunsu Kim, Yungi Kim, Chanjun Park


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.
Anthology ID:
2024.emnlp-demo.3
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Delia Irazu Hernandez Farias, Tom Hope, Manling Li
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
25–33
Language:
URL:
https://aclanthology.org/2024.emnlp-demo.3
DOI:
10.18653/v1/2024.emnlp-demo.3
Bibkey:
Cite (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.
Cite (Informal):
Evalverse: Unified and Accessible Library for Large Language Model Evaluation (Kim et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-demo.3.pdf