English

Evalverse: Unified and Accessible Library for Large Language Model Evaluation

Computation and Language 2024-10-08 v2 Artificial Intelligence

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.

Keywords

Cite

@article{arxiv.2404.00943,
  title  = {Evalverse: Unified and Accessible Library for Large Language Model Evaluation},
  author = {Jihoo Kim and Wonho Song and Dahyun Kim and Yunsu Kim and Yungi Kim and Chanjun Park},
  journal= {arXiv preprint arXiv:2404.00943},
  year   = {2024}
}

Comments

Accepted to EMNLP 2024 Demo Track

R2 v1 2026-06-28T15:39:58.817Z