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Evalverse: Unified and Accessible Library for Large Language Model Evaluation

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

TL;DR

Evalverse serves as a powerful tool for the comprehensive assessment of LLMs, offering both researchers and practitioners a centralized and easily accessible evaluation framework.

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.

Evalverse: Unified and Accessible Library for Large Language Model Evaluation

TL;DR

Evalverse serves as a powerful tool for the comprehensive assessment of LLMs, offering both researchers and practitioners a centralized and easily accessible evaluation framework.

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.
Paper Structure (32 sections, 4 figures, 5 tables)

This paper contains 32 sections, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work and Background
  3. LLM Evaluation Aspects
  4. General performance.
  5. Performance for chat applications.
  6. Performance for RAG.
  7. Performance for various domains.
  8. LLM Evaluation Frameworks
  9. Evalverse
  10. Why Evalverse?
  11. Evalverse Architecture
  12. Submodule.
  13. Connector.
  14. Evaluator.
  15. Compute Cluster.
  16. ...and 17 more sections

Figures (4)

  • Figure 1: Overview of Evalverse. Users can interact with Evalverse in a no-code manner. External benchmark frameworks are integrated as submodules.
  • Figure 2: The system architecture of Evalverse. Users can use the Evaluator directly for code-based evaluation, or interact with the Reporter for a no-code approach to LLM evaluation.
  • Figure 3: No-code evaluation request with Slack bot.
  • Figure 4: No-code evaluation report with Slack bot.