Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and 2.1 Automatic Evaluation Methods for LLMs 2 Background and 2.1 Automatic Evaluation Methods for LLMs 2.2 Meta-Evaluation of LLMs 2.2 Meta-Evaluation of LLMs 3 Design and Implementation and 3.1 Design Principles 3 Design and Implementation and 3.1 Design Principles 3.2 FreeEval Architecture Overview and 3.3 Extensible Modular Design 3.2 FreeEval Architecture Overview and 3.3 Extensible Modular Design 3.4 Trustworthy Evaluation 3.4 Trustworthy Evaluation 3.5 Efficient Inference Backends 3.5 Efficient Inference Backends 4 Conclusion, Ethical Considerations, and References 4 Conclusion, Ethical Considerations, and References 3 Design and Implementation In this section, we present the design and implementation of FreeEval, we discuss the framework’s architecture, its key components, and how they address the challenges identified previously. 3.1 Design Principles To build a flexible, efficient research tool for LLM evaluation we make sure the architecture of FreeEval follows the following principles: • Modular: FreeEval provides a modular architecture that allows for easy integration of new evaluation methods, datasets, and protocols. This modularity also ensures transparency by making all evaluation settings and details openly accessible to users. Modular • Trustworthy: The evaluation results must be trustworthy, and the evaluation process should be fair and effective. FreeEval allows users to propose new evaluation methods, with a comprehensive meta-evaluation proving its soundness. Trustworthy • Efficient: FreeEval prioritizes efficiency to minimize the high computational costs associated with LLM inference. By focusing on cost-effective evaluation processes, researchers can conduct large-scale evaluations while effectively managing computational resources and financial costs. Efficient This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Zhuohao Yu, Peking University;
(2) Chang Gao, Peking University;
(3) Wenjin Yao, Peking University;
(4) Yidong Wang, Peking University;
(5) Zhengran Zeng, Peking University;
(6) Wei Ye, Peking University and a corresponding author;
(7) Jindong Wang, Microsoft Research;
(8) Yue Zhang, Westlake University;
(9) Shikun Zhang, Peking University. Authors: Authors: (1) Zhuohao Yu, Peking University; (2) Chang Gao, Peking University; (3) Wenjin Yao, Peking University; (4) Yidong Wang, Peking University; (5) Zhengran Zeng, Peking University; (6) Wei Ye, Peking University and a corresponding author; (7) Jindong Wang, Microsoft Research; (8) Yue Zhang, Westlake University; (9) Shikun Zhang, Peking University.

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