  In this study, we focus on two main tasks, the first for detecting legal
violations within unstructured textual data, and the second for associating
these violations with potentially affected individuals. We constructed two
datasets using Large Language Models (LLMs) which were subsequently validated
by domain expert annotators. Both tasks were designed specifically for the
context of class-action cases. The experimental design incorporated fine-tuning
models from the BERT family and open-source LLMs, and conducting few-shot
experiments using closed-source LLMs. Our results, with an F1-score of 62.69\%
(violation identification) and 81.02\% (associating victims), show that our
datasets and setups can be used for both tasks. Finally, we publicly release
the datasets and the code used for the experiments in order to advance further
research in the area of legal natural language processing (NLP).
