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Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing (NLLP) skill. Our goal is to use LPE with LLMs over long legal documents for the Legal Judgement Prediction (LJP) task. We investigate the performance of zero-shot LPE for given facts in case-texts from the European Court of Human Rights (in English) and the Federal Supreme Court of Switzerland (in German, French and Italian). Our results show that zero-shot LPE is better compared to the baselines, but it still falls short compared to current state of the art supervised approaches. Nevertheless, the results are important, since there was 1) no explicit domain-specific data used - so we show that the transfer to the legal domain is possible for general-purpose LLMs, and 2) the LLMs where directly applied without any further training or fine-tuning - which in turn saves immensely in terms of additional computational costs.

Despite considerable recent progress, the creation of well-balanced and diverse resources remains a time-consuming and costly challenge in Argument Mining. Active Learning reduces the amount of data necessary for the training of machine learning models by querying the most informative samples for annotation and therefore is a promising method for resource creation. In a large scale comparison of several Active Learning methods, we show that Active Learning considerably decreases the effort necessary to get good deep learning performance on the task of Argument Unit Recognition and Classification (AURC).

Optimal walking trajectories are essential for bipedal walking. Recent developments enabled the use of motion planer based on nonlinear optimization. They are however, not directly applicable in real-time tasks due to a high computation time. Therefore, a task space consisting of the stride-length and a step time is used to precompute corresponding trajectory parameters in a certain range regarding a cost function. The resulting trajectories define an optimal joint space motion. The mapping from the task space to the joint space is generalized with multiple machine learning methods. A parametrization of every method was determined to represent the underlying model of the data as good as possible, without over-fitting. Finally, the performance regarding the accuracy and runtime and the evaluation of the cost value and constraint violation in the walking tasks is discussed.