The Design and Implementation of FreeEval

Table of Links

Abstract and 1 Introduction

2 Background and 2.1 Automatic Evaluation Methods for LLMs

2.2 Meta-Evaluation of LLMs

3 Design and Implementation and 3.1 Design Principles

3.2 FreeEval Architecture Overview and 3.3 Extensible Modular Design

3.4 Trustworthy Evaluation

3.5 Efficient Inference Backends

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.

• 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.

• 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.