GRAF : Graph Retrieval Augmented by Facts for R omanian Legal Multi-Choice Question Answering (2025)

Abstract: Pre-trained language models have shown remarkable performance in recent years, setting a new paradigm for natural language processing (NLP) research. The legal domain has received some attention from the NLP community, in part due to its textual nature. Question answering (QA) systems represent some of the tasks in this domain. This work explores the legal multiple-choice QA (MCQA) for Romanian. The contribution of this work is multi-fold. We introduce JuRO, the first openly available Romanian legal MCQA dataset, comprising 10,836 questions from three examinations. Along with this dataset, we introduce CROL, an organized corpus of laws comprising a total of 93 distinct documents with their modifications over 763 time spans, which we used for information retrieval techniques in this work. Additionally, we construct Law-RoG, the first graph of legal knowledge for the Romanian language, derived from the aforementioned corpus. Lastly, we propose a novel approach for MCQA, namely Graph Retrieval Augmented by Facts (GRAF), which achieves competitive results with generally accepted state-of-the-art methods and even exceeds them in most settings.

## introduction question answering (qa) represents a family of downstream natural language processing (nlp) tasks explored in various settings @xcite @xcite . some qa tasks can be formulated as open-ended questions that require an elaborate answer, which may include the rationale behind it @xcite . other tasks focus on retrieving the answer from a given context. in this * corresponding author. scenario, the answer can either be explicitly found in the provided context, in which case a model has to identify the location at which the answer occurs @xcite , or infer the correct answer based on understanding and interpretation of the context at hand, a task known as machine reading comprehension @xcite . the tasks mentioned above have been explored for various domains and languages @xcite @xcite @xcite . our work focuses on multiple-choice question answering (mcqa) for the romanian legal domain. legal qa represents an emerging area of research due to the insights it can provide in addressing various other problems, such as the dialogue system @xcite . from a practical perspective, the average citizen of a nation can benefit from the opportunity to find answers to legal inquiries alongside experts in the field, which can lead to increased productivity. additionally, natural language corpora for the romanian language are scarce. a dataset for single-choice qa was recently performed by @xcite , which proposed a romanian medical mcqa dataset. although the romanian legal domain has been tackled in the past @xcite , no open-source datasets are available. this motivated us to introduce new comprehensive resources for the romanian language. in summary, the contributions of this paper are multifold: ## graf we introduce a novel approach for retrieving information from a kg, which we applied to the legal mcqa. the same principles discussed regarding claim checking and validation can be applied to other tasks requiring factual knowledge. ## experiments and results in this section, we present the results of our extensive experimentation and discuss the findings obtained. ## conclusions in this paper, we have introduced new resources for the romanian legal domain, being the first to construct and release a romanian legal kg. we also proposed a novel approach for kg ir, namely graf, which surpassed the baselines proposed for mcqa.