The advent of Generative AI, particularly through Large Language Models
(LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI
landscape. Advanced LLMs exhibit multimodality, handling diverse data formats,
thereby broadening their application scope. However, the complexity and
emergent autonomy of these models introduce challenges in predictability and
legal compliance. This paper delves into the legal and regulatory implications
of Generative AI and LLMs in the European Union context, analyzing aspects of
liability, privacy, intellectual property, and cybersecurity. It critically
examines the adequacy of the existing and proposed EU legislation, including
the Artificial Intelligence Act (AIA) draft, in addressing the unique
challenges posed by Generative AI in general and LLMs in particular. The paper
identifies potential gaps and shortcomings in the legislative framework and
proposes recommendations to ensure the safe and compliant deployment of
generative models, ensuring they align with the EU's evolving digital landscape
and legal standards.

生成式人工智能和大型语言模型的出现标志着人工智能领域的范式转变。本文分析欧盟背景下产生式人工智能和大型语言模型的法律和监管影响，着重分析责任、隐私、知识产权和网络安全等方面。它批判性地审视现有和拟议的欧盟立法，包括《人工智能法案（AIA）》草案，以应对生成式人工智能和大型语言模型所带来的独特挑战。本文确定了立法框架中的潜在差距和不足，并提出建议，以确保生成模型的安全合规部署，使其与欧盟不断演变的数字环境和法律标准保持一致。

欧盟法律中的生成人工智能：责任、隐私、知识产权和网络安全

Generative AI in EU Law: Liability, Privacy, Intellectual Property, and  Cybersecurity

Legal language can be understood as the language typically used by those
engaged in the legal profession and, as such, it may come both in spoken or
written form. Recent legislation on cybersecurity obviously uses legal language
in writing, thus inheriting all its interpretative complications due to the
typical abundance of cases and sub-cases as well as to the general richness in
detail. This paper faces the challenge of the essential interpretation of the
legal language of cybersecurity, namely of the extraction of the essential
Parts of Speech (POS) from the legal documents concerning cybersecurity. The
challenge is overcome by our methodology for POS tagging of legal language. It
leverages state-of-the-art open-source tools for Natural Language Processing
(NLP) as well as manual analysis to validate the outcomes of the tools. As a
result, the methodology is automated and, arguably, general for any legal
language following minor tailoring of the preprocessing step. It is
demonstrated over the most relevant EU legislation on cybersecurity, namely on
the NIS 2 directive, producing the first, albeit essential, structured
interpretation of such a relevant document. Moreover, our findings indicate
that tools such as SpaCy and ClausIE reach their limits over the legal language
of the NIS 2.

该论文探讨了针对网络安全法律文件的词性标注方法，使用 SpaCy 和 ClausIE 等工具对欧盟法律文书进行分析并得出结论，提出了一种自动化方法，并得出了第一个针对 NIS 2 指令的结构化解释。