title: On the Trustworthiness of Generative Foundation Models: Guideline,
    Assessment, and Perspective
summary: 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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paper_title: On the Trustworthiness of Generative Foundation Models: Guideline,
    Assessment, and Perspective
paper_summary: 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.