F ree E val: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models (2024)

Abstract: The rapid growth of evaluation methodologies and datasets for large language models (LLMs) has created a pressing need for their unified integration. Meanwhile, concerns about data contamination and bias compromise the trustworthiness of evaluation findings, while the efficiency of evaluation processes remains a bottleneck due to the significant computational costs associated with LLM inference.In response to these challenges, we introduce FreeEval, a modular framework not only for conducting trustworthy and efficient automatic evaluations of LLMs but also serving as a platform to develop and validate new evaluation methodologies. FreeEval addresses key challenges through: (1) unified abstractions that simplify the integration of diverse evaluation methods, including dynamic evaluations requiring complex LLM interactions; (2) built-in meta-evaluation techniques such as data contamination detection and human evaluation to enhance result fairness; (3) a high-performance infrastructure with distributed computation and caching strategies for efficient large-scale evaluations; and (4) an interactive Visualizer for result analysis and interpretation to support innovation of evaluation techniques. We open-source all our code at https://github.com/WisdomShell/FreeEval and our demostration video, live demo, installation guides are available at: https://freeeval.zhuohao.me/.

## introduction large language models (llms) have revolutionized natural language processing (nlp) with their impressive performance across various tasks @xcite @xcite . as llms play a critical role in academia and industry, evaluating their capabilities has become essential @xcite . consequently, researchers have proposed automatic evaluation methodologies using benchmark datasets @xcite @xcite for objective assessments, and llm-based subjective evaluation tools @xcite @xcite . the rapid emergence of evaluation data and methods has intensified the challenge of incorporating cutting-edge techniques cost-effectively and conducting reliable evaluations. in response, several open-source evaluation platforms for llms have been proposed, each with unique features. despite these promising efforts, current evaluation platforms still face three bottlenecks. first, a unified and extensible framework is required to integrate evaluation methods seamlessly. this consequently affects the flexibility, transparency, and interpretability of the evaluation. the evaluation results are highly dependent on the deployment settings and prompting techniques, as llms are not robust enough for these intricate settings @xcite . for example, second, the reliability of results from these platforms cannot always be guaranteed. automatic evaluation of llms remains a challenging to address these challenges, we propose freeeval, a modular and extensible framework for trustworthy and efficient automatic evaluation of llms, as well as a platform for developing new evaluation methodologies. the main features of freeeval are: unified abstraction and modular implementation of various evaluation methods. we introduce concepts of step, dataset, and config to uniformly describe dataset-based, classic referencebased, and llm-based evaluators. dataset-based evaluators include task-specific datasets along with dataset operations such as custom prompting, data augmenting and generation. llm-based evaluators, such as mt-bench @xcite , alpacaeval @xcite , pandalm @xcite and kieval @xcite , are also integrated to provide subjective assessment. classic judges, which utilize reference-based evaluation metrics like rouge @xcite and bertscore @xcite to examine model output. freeeval's modular design allows for easy implementation of new evaluation protocols and supports evaluating both open-source and proprietary models. the abstractions also bring transparency to the evaluation process since all the evaluation settings are open to users. practical meta-evaluation modules for trustworthiness. freeeval incorporates contamination detection, human judgment, case analysis, and bias evaluation. these features mitigate overfitting risks, enhance interpretability, and support the development and validation of new evaluation methods. a user-friendly interface for human annotation further improves explainability and reliability of results. optimized distributed and concurrent inference with load balancing and caching mechanisms. leveraging cutting-edge inference engines with concurrency and caching strategies, freeeval efficiently handles large-scale evaluations on multinode multi-gpu clusters. this infrastructure supports both open-source models and proprietary apis, ensuring scalability and cost-effectiveness. intuitive visualizer for result analysis and interpretation. this component provides interactive tools for exploring results, conducting case studies, and identifying patterns. it enhances interpretability and supports the development of new evaluation methods through visual feedback. by combining these features, freeeval addresses key challenges in llm evaluation while serving as a powerful platform for researchers to build new evaluation methods. ## background in this section, we provide an overview of the current landscape of llm evaluation methods, the challenges posed by data contamination, and the importance of meta-evaluation in assessing the reliability and validity of evaluation protocols. ## 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. ## conclusion we introduce freeeval, a modular and extensible framework for trustworthy and efficient automatic evaluation of llms. freeeval innovatively addresses key challenges in llm evaluation by providing a unified implementation of various evaluation methods, incorporating meta-evaluation modules, and leveraging high-performance inference backends. the framework's modular design facilitates easy integration of new evaluation protocols and improves transparency. the integrated visualizer enhances result interpretation and analysis, supporting comprehensive evaluation and the development of new methodologies. we will continue to maintain and expand the freeeval toolkit, striving to provide deeper insights into the capabilities and limitations of llms and contribute to the development of more robust and trustworthy language models.