  The SemEval task on Argument Reasoning in Civil Procedure is challenging in
that it requires understanding legal concepts and inferring complex arguments.
Currently, most Large Language Models (LLM) excelling in the legal realm are
principally purposed for classification tasks, hence their reasoning rationale
is subject to contention. The approach we advocate involves using a powerful
teacher-LLM (ChatGPT) to extend the training dataset with explanations and
generate synthetic data. The resulting data are then leveraged to fine-tune a
small student-LLM. Contrary to previous work, our explanations are not directly
derived from the teacher's internal knowledge. Instead they are grounded in
authentic human analyses, therefore delivering a superior reasoning signal.
Additionally, a new `mutation' method generates artificial data instances
inspired from existing ones. We are publicly releasing the explanations as an
extension to the original dataset, along with the synthetic dataset and the
prompts that were used to generate both. Our system ranked 15th in the SemEval
competition. It outperforms its own teacher and can produce explanations
aligned with the original human analyses, as verified by legal experts.
