Legal work, characterized by its text-heavy and resource-intensive nature, presents unique challenges and opportunities for NLP research. While data-driven approaches have advanced the field, their lack of interpretability and trustworthiness limits their applicability in dynamic legal environments. To address these issues, we collaborated with legal experts to extend an existing dataset and explored the use of Large Language Models (LLMs) and in-context learning to evaluate the legality of clauses in German employment contracts. Our work evaluates the ability of different LLMs to classify clauses as "valid," "unfair," or "void" under three legal context variants: no legal context, full-text sources of laws and court rulings, and distilled versions of these (referred to as examination guidelines). Results show that full-text sources moderately improve performance, while examination guidelines significantly enhance recall for void clauses and weighted F1-Score, reaching 80\%. Despite these advancements, LLMs' performance when using full-text sources remains substantially below that of human lawyers. We contribute an extended dataset, including examination guidelines, referenced legal sources, and corresponding annotations, alongside our code and all log files. Our findings highlight the potential of LLMs to assist lawyers in contract legality review while also underscoring the limitations of the methods presented.

The Korean Intellectual Property Office (KIPO) announced Patent Examination Guidelines for key technology areas related to the Fourth Industrial Revolution, including machine learning based artificial intelligence ("AI"), on January 18, 2021. In the Examination Guidelines for AI, KIPO outlines specific guidelines on description and novelty/inventiveness requirements for different categories of AI inventions (e.g., AI model training invention and AI application invention, as depicted below), in addition to eligibility requirements which correspond to that of computer-related inventions. In particular, KIPO's Examination Guidelines provide examples of various AI inventions with practical drafting tips on enablement (Article 42(3)(i) of Patent Act) and inventiveness requirements (Article 29(2)). Under Article 42(3)(i), the description of an invention shall be written clearly and fully so that a person with ordinary skill in the art (POSITA) to which the invention pertains can easily practice the claimed invention. For an AI invention, KIPO suggests that the description of the technical problem, solution, and specific technical configuration (e.g., training data, data preprocessing, trained model, and loss function, etc.) be included to enable a POSITA to practice the claimed invention, unless the technical configuration is well known in the art.