TRUSTEVAL : A Dynamic Evaluation Toolkit on Trustworthiness of Generative Foundation Models (2025)

Abstract: Ensuring the trustworthiness of Generative Foundation Models (GenFMs) is a pressing challenge as they gain widespread use. Existing evaluation toolkits are often limited in scope, dynamism, and flexibility. This paper introduces TRUSTEVAL, a dynamic and comprehensive toolkit designed for evaluating GenFMs across various dimensions. TRUSTEVAL supports both dynamic dataset generation and evaluation, offering advanced features including comprehensiveness, usability, and flexibility. TRUSTEVAL integrates diverse generative models, datasets, evaluation methods, metrics, inference efficiency enhancement, and evaluation report generation. Through case studies, we demonstrate TRUSTEVAL’s potential to advance the trustworthiness evaluation of GenFMs.

## introduction in recent years, foundation models, defined as large-scale pre-trained models (such as gpt @xcite and bert @xcite @xcite ) that can support a wide range of downstream tasks @xcite , have had a major impact on generative modeling. when these models are adapted for generative tasks, they are referred to as generative foundation models (genfms) @xcite . as genfms gain widespread adoption across diverse industries, ensuring their trustworthiness has emerged as a critical concern. numerous studies have highlighted potential trustworthiness challenges associated with these models, including adversarial attacks @xcite @xcite , hallucinations @xcite †: correspondence to: xiangliang zhang (xzhang33@nd. edu) *: equal contribution 2023), misinformation @xcite , biases @xcite , privacy leaks @xcite , and more. despite their significant progress, existing toolkits for evaluating the trustworthiness of genfms @xcite @xcite face several limitations, including: (1) lack of comprehensiveness (e.g., focusing solely on certain model types or evaluation dimensions), (2) limited dynamism and diversity (e.g., reliance on static prompt templates and datasets), and (3) insufficient flexibility and uniformity (e.g., inability to customize evaluation config and standards). to address these challenges, we propose truste-val 1 , a toolkit designed to provide a dynamic, comprehensive, and unified framework for evaluating the trustworthiness of genfms. overall, truste-val offers two primary functionalities: • dynamic dataset generation. trusteval enables dynamic and diverse datasets creation, including: (1) a metadata curator for collecting metadata using strategies like web-browsing agents @xcite . (2) a test case builder for generating test cases based on the collected metadata. (3) a contextual variator to ensure cases are varied and representative across different contexts, mitigating prompt sensitivity. • evaluation of generative models. truste-val provides a unified platform for evaluating the trustworthiness of genfms, supporting t2i models, llms, and vlms. it also integrates streamlined interfaces for local inference and api-based inference, along with diverse trustworthiness evaluation methods and metrics. beyond these core functionalities, truste-val also provides the following features: • comprehensiveness. trusteval supports aspect dynamic diverse dataset gen. dataset eval. #trustworthiness dims. flexible support model trusteval (ours) ✓ ✓ ✓ ✓ 7 ✓ trustllm (huang et al., 2024b) ✓ 6 heim (lee et al., 2024b) ✓ 3 multitrust (zhang et al., 2024b) ✓ 5 mllm-guard (gu et al., 2024) ✓ 5 helm (liang et al., 2022) ✓ 3 llm harness (gao et al., 2024) ✓ 2 opencompass (contributors, 2023) ✓ ✓ 1 dyval series (zhu et al., 2023a, 2024) ✓ ✓ ✓ unigen (wu et al., 2024a) ✓ ✓ ✓ ✓ autobencher (li et al., 2024a) ✓ ✓ ✓ ✓ ✓ ## trusteval in this section, we provide a detailed introduction to trusteval, focusing on its two key functionalities: dynamic dataset generation and trustworthiness evaluation of genfms. an illustration of the overall framework is provided in figure 1 . ## case study: dataset generation and evaluation in this section, we focus on the code and implementation details for dynamic dataset generation. the detailed code for model inference and evaluation is provided in appendix a for reference. ## conclusion we introduce trusteval, a comprehensive and user-friendly toolkit for evaluating the trustworthiness of genfms. through its integrated pipeline for dataset generation and evaluation, truste-val enables effective trustworthiness assessments across multiple dimensions. we will maintain and expand trusteval, as only collective effort can build truly trustworthy genfms.