Retrieval-augmented generation (RAG) systems can play an important role in making law more accessible. However, large and reliable resources for training and benchmarking such systems remain scarce, especially for under-resourced languages like Dutch. To address this gap, and building on previous work (Louis et al., 2024), we introduce bLLeQA, a bilingual parallel question-answering dataset grounded in Belgian legal resources, both in French and Dutch. The dataset contains aligned questions, answers, and supporting articles in both languages, enabling evaluation of both retrieval and end-to-end RAG pipelines. Using bLLeQA, we benchmark the full RAG pipeline in a zero-shot setting, covering retrieval, citation extraction, refusal behavior, and generation quality. Our experiments show that open-weight models are competitive with proprietary models in retrieval and citation extraction, but lag behind in generation quality in the RAG pipeline. Across all models, refusal capability remains weak, meaning that models do not reliably detect when the provided supporting sources are incomplete. In addition, the end-to-end RAG setup still yields a substantial share of flawed responses, reaching 20% even in the best-case scenario.

Recently, embedding resources, including models, benchmarks, and datasets, have been widely released to support a variety of languages. However, the Dutch language remains underrepresented, typically comprising only a small fraction of the published multilingual resources. To address this gap and encourage the further development of Dutch embeddings, we introduce new resources for their evaluation and generation. First, we introduce the Massive Text Embedding Benchmark for Dutch (MTEB-NL), which includes both existing Dutch datasets and newly created ones, covering a wide range of tasks. Second, we provide a training dataset compiled from available Dutch retrieval datasets, complemented with synthetic data generated by large language models to expand task coverage beyond retrieval. Finally, we release a series of E5-NL compact yet efficient embedding models that demonstrate strong performance across multiple tasks. We make our resources publicly available through the Hugging Face Hub and the MTEB package.