On the Trustworthiness of Generative Foundation Models: Guideline,
  Assessment, and Perspective
Generative Foundation Models (GenFMs) have emerged as transformative tools.
However, their widespread adoption raises critical concerns regarding
trustworthiness across dimensions. This paper presents a comprehensive
framework to address these challenges through three key contributions. First,
we systematically review global AI governance laws and policies from
governments and regulatory bodies, as well as industry practices and standards.
Based on this analysis, we propose a set of guiding principles for GenFMs,
developed through extensive multidisciplinary collaboration that integrates
technical, ethical, legal, and societal perspectives. Second, we introduce
TrustGen, the first dynamic benchmarking platform designed to evaluate
trustworthiness across multiple dimensions and model types, including
text-to-image, large language, and vision-language models. TrustGen leverages
modular components--metadata curation, test case generation, and contextual
variation--to enable adaptive and iterative assessments, overcoming the
limitations of static evaluation methods. Using TrustGen, we reveal significant
progress in trustworthiness while identifying persistent challenges. Finally,
we provide an in-depth discussion of the challenges and future directions for
trustworthy GenFMs, which reveals the complex, evolving nature of
trustworthiness, highlighting the nuanced trade-offs between utility and
trustworthiness, and consideration for various downstream applications,
identifying persistent challenges and providing a strategic roadmap for future
research. This work establishes a holistic framework for advancing
trustworthiness in GenAI, paving the way for safer and more responsible
integration of GenFMs into critical applications. To facilitate advancement in
the community, we release the toolkit for dynamic evaluation.
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Xiangliang Zhang
On the Trustworthiness of Generative Foundation Models: Guideline,
  Assessment, and Perspective
Generative Foundation Models (GenFMs) have emerged as transformative tools.
However, their widespread adoption raises critical concerns regarding
trustworthiness across dimensions. This paper presents a comprehensive
framework to address these challenges through three key contributions. First,
we systematically review global AI governance laws and policies from
governments and regulatory bodies, as well as industry practices and standards.
Based on this analysis, we propose a set of guiding principles for GenFMs,
developed through extensive multidisciplinary collaboration that integrates
technical, ethical, legal, and societal perspectives. Second, we introduce
TrustGen, the first dynamic benchmarking platform designed to evaluate
trustworthiness across multiple dimensions and model types, including
text-to-image, large language, and vision-language models. TrustGen leverages
modular components--metadata curation, test case generation, and contextual
variation--to enable adaptive and iterative assessments, overcoming the
limitations of static evaluation methods. Using TrustGen, we reveal significant
progress in trustworthiness while identifying persistent challenges. Finally,
we provide an in-depth discussion of the challenges and future directions for
trustworthy GenFMs, which reveals the complex, evolving nature of
trustworthiness, highlighting the nuanced trade-offs between utility and
trustworthiness, and consideration for various downstream applications,
identifying persistent challenges and providing a strategic roadmap for future
research. This work establishes a holistic framework for advancing
trustworthiness in GenAI, paving the way for safer and more responsible
integration of GenFMs into critical applications. To facilitate advancement in
the community, we release the toolkit for dynamic evaluation.
A framework is provided for enhancing the trustworthiness of Generative Foundation Models via comprehensive governance principles and the introduction of TrustGen, a benchmarking platform that evaluates trustworthiness across diverse GenFMs.
Generative Foundation Models GenFMs AI governance guiding principles multidisciplinary collaboration TrustGen dynamic benchmarking platform metadata curation test case generation contextual variation iterative assessments trustworthiness text-to-image large language models vision-language models GenAI