The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of responsible AI -- transparency -- is largely missing from the current discourse around LLMs. It is paramount to pursue new approaches to provide transparency for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so with a human-centered perspective: Transparency is fundamentally about supporting appropriate human understanding, and this understanding is sought by different stakeholders with different goals in different contexts. In this new era of LLMs, we must develop and design approaches to transparency by considering the needs of stakeholders in the emerging LLM ecosystem, the novel types of LLM-infused applications being built, and the new usage patterns and challenges around LLMs, all while building on lessons learned about how people process, interact with, and make use of information. We reflect on the unique challenges that arise in providing transparency for LLMs, along with lessons learned from HCI and responsible AI research that has taken a human-centered perspective on AI transparency. We then lay out four common approaches that the community has taken to achieve transparency -- model reporting, publishing evaluation results, providing explanations, and communicating uncertainty -- and call out open questions around how these approaches may or may not be applied to LLMs. We hope this provides a starting point for discussion and a useful roadmap for future research.

翻译：强大语言模型（LLM）的兴起带来了巨大的创新机遇，也同时为个人和社会整体带来了潜在风险。我们正处于确保LLM及LLM融合应用被负责任地开发与部署的关键时刻。然而，负责任人工智能的核心支柱——透明度——在当前关于LLM的讨论中基本缺失。寻求提供LLM透明度的新方法至关重要，而人工智能与人机交互（HCI）交叉领域的多年研究表明，我们必须从以人为中心的视角来推进：透明度本质上关乎支持恰当的人类理解，这种理解由不同情境下持有不同目标的不同利益相关方所追求。在这个LLM新时代，我们必须通过考虑新兴LLM生态系统中利益相关方的需求、正在构建的新型LLM融合应用类型，以及围绕LLM产生的新使用模式和挑战，来开发和设计透明度方法，同时以人们如何处理、交互及利用信息的经验教训为基础。我们反思了为LLM提供透明度所面临的独特挑战，以及从HCI和以人为中心视角研究人工智能透明度的负责任人工智能研究中汲取的经验。随后，我们阐述了学界为实现透明度通常采用的四种方法——模型报告、发布评估结果、提供解释以及沟通不确定性——并提出了这些方法如何（或是否）可应用于LLM的开放性问题。我们希望这能为讨论提供一个起点，并为未来研究提供一份有用的路线图。