Evalverse: Unified and Accessible Library for Large Language Model
  Evaluation
Jihoo Kim Wonho Song Dahyun Kim Yunsu Kim Yungi Kim Chanjun Park
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.