Before using GenAI models as EdTech tools, their pedagogical suitability should be corroborated. In this paper, we present ShAnEL-2, a novel multilingual dataset comprising 1,185 student responses to short-answer language learning exercises corrected by teachers. We use ShAnEL-2 to establish an initial benchmark of (1) “off-the-shelf” GenAI models and (2) retrieval-augmented generation (RAG) techniques for the automated correction of this exercise type. With an overall accuracy of 90% and recall of 95%, few-shot RAG (which adds previously corrected responses to the prompt) outperforms the off-the-shelf baseline and textbook RAG setup (which adds coursebook materials) by up to 7 (accuracy) and 5 (recall) percentage points. These results confirm that LLMs learn better from examples than from analysing context and highlight GenAI’s particular potential as a correction assistant for teachers.

Effective communication in asylum reception settings requires reliable machine translation (MT) across many languages, including low-resource ones. Using data from the MaTIAS project, we compare retrieval-augmented LLM translation with adaptive Neural MT across 14 target languages with varying resource levels. Working with a very small translation memory of only 358 sentences, we evaluate fuzzy match (FM) augmentation as an in-context learning strategy for open-source and commercial LLMs and benchmark these against ModernMT with and without domain adaptation. In the LLM setting, FM-based example selection consistently outperforms random selection and zero-shot prompting, with the largest gains for low-resource languages. Adaptive NMT retains an overall advantage, although Gemini Pro approaches its performance and outperforms it on 6 of 14 languages, highlighting a trade-off between translation quality and data sovereignty in privacy-sensitive contexts. These findings show that FM augmentation remains effective under severe data constraints and emphasise the importance of language-specific evaluation in multilingual MT.

To enhance the accessibility of scientific literature in multiple languages and facilitate the exchange of information among scholars and a wider audience, there is a need for high-performing specialized machine translation (MT) engines. However, this requires efficient filtering and the use of domain-specific data. In this study, we investigate whether approaches for increasing training data using topic filtering and more efficient use of such data through exploiting fuzzy matches (i.e. similar translations to a given input; FMs) improve translation quality. We apply these techniques both to sequence-to-sequence MT models and off-the-shelf multilingual large language models (LLMs) in three scientific disciplines. Our results suggest that the combination of topic filtering and FM augmentation is an effective strategy for training neural machine translation (NMT) models from scratch, not only surpassing baseline NMT models but also delivering improved translation performance compared to smaller LLMs in terms of the number of parameters. Furthermore, we find that although FM augmentation through in-context learning generally improves LLM translation performance, limited domain-specific datasets can yield results comparable to those achieved with additional multi-domain datasets.