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FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models

Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang

TL;DR

FreeEval tackles the fragmentation in LLM evaluation by providing a unified, modular framework that supports diverse evaluation methods while enforcing trustworthiness via meta-evaluation. It introduces config-driven pipelines built from step, dataset, and config abstractions, alongside data-contamination detection and human-bias assessments to improve reliability. Its high-performance inference backends with caching, load-balancing, and multi-node deployment enable efficient large-scale evaluations across open-source and proprietary LLMs. This framework promises more reproducible, fair, and cost-efficient evaluations and could accelerate robust benchmarking in the LLM community.

Abstract

The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-effectively while ensuring reliability, reproducibility, and efficiency. Currently, there is a notable absence of a unified and adaptable framework that seamlessly integrates various evaluation approaches. Moreover, the reliability of evaluation findings is often questionable due to potential data contamination, with the evaluation efficiency commonly overlooked when facing the substantial costs associated with LLM inference. In response to these challenges, we introduce FreeEval, a modular and scalable framework crafted to enable trustworthy and efficient automatic evaluations of LLMs. Firstly, FreeEval's unified abstractions simplify the integration and improve the transparency of diverse evaluation methodologies, encompassing dynamic evaluation that demand sophisticated LLM interactions. Secondly, the framework integrates meta-evaluation techniques like human evaluation and data contamination detection, which, along with dynamic evaluation modules in the platform, enhance the fairness of the evaluation outcomes. Lastly, FreeEval is designed with a high-performance infrastructure, including distributed computation and caching strategies, enabling extensive evaluations across multi-node, multi-GPU clusters for open-source and proprietary LLMs.

FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models

TL;DR

FreeEval tackles the fragmentation in LLM evaluation by providing a unified, modular framework that supports diverse evaluation methods while enforcing trustworthiness via meta-evaluation. It introduces config-driven pipelines built from step, dataset, and config abstractions, alongside data-contamination detection and human-bias assessments to improve reliability. Its high-performance inference backends with caching, load-balancing, and multi-node deployment enable efficient large-scale evaluations across open-source and proprietary LLMs. This framework promises more reproducible, fair, and cost-efficient evaluations and could accelerate robust benchmarking in the LLM community.

Abstract

The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-effectively while ensuring reliability, reproducibility, and efficiency. Currently, there is a notable absence of a unified and adaptable framework that seamlessly integrates various evaluation approaches. Moreover, the reliability of evaluation findings is often questionable due to potential data contamination, with the evaluation efficiency commonly overlooked when facing the substantial costs associated with LLM inference. In response to these challenges, we introduce FreeEval, a modular and scalable framework crafted to enable trustworthy and efficient automatic evaluations of LLMs. Firstly, FreeEval's unified abstractions simplify the integration and improve the transparency of diverse evaluation methodologies, encompassing dynamic evaluation that demand sophisticated LLM interactions. Secondly, the framework integrates meta-evaluation techniques like human evaluation and data contamination detection, which, along with dynamic evaluation modules in the platform, enhance the fairness of the evaluation outcomes. Lastly, FreeEval is designed with a high-performance infrastructure, including distributed computation and caching strategies, enabling extensive evaluations across multi-node, multi-GPU clusters for open-source and proprietary LLMs.
Paper Structure (12 sections, 3 figures, 3 tables)

This paper contains 12 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Automatic Evaluation Methods for LLMs
  4. Meta-Evaluation of LLMs
  5. Design and Implementation
  6. Design Principles
  7. FreeEval Architecture Overview
  8. Extensible Modular Design
  9. Trustworthy Evaluation
  10. Efficient Inference Backends
  11. Conclusion
  12. Ethical Conciderations

Figures (3)

  • Figure 1: Overall architecture of FreeEval.
  • Figure 2: Config for an example pipeline, evaluating LLaMA-2 70B touvron2023llama2 on ARC-Challenge clark2018arc dataset and then detecting data contamination with Min-K% Prob shi2023detecting.
  • Figure 3: Example code for running FreeEval's inference backends. We rely on these backends for efficient inference and provide a simple abstraction.