Multilingual large language models (LLMs) often exhibit emergent ‘shadow’ capabilities in languages without official support, yet their performance on these languages remains uneven and under-measured. This is particularly acute for morphosyntactically rich minority languages such as Scottish Gaelic, where translation benchmarks fail to capture structural competence. We introduce GaelEval, the first multi-dimensional benchmark for Gaelic, comprising: (i) an expert-authored morphosyntactic MCQA task; (ii) a culturally-grounded translation benchmark and (iii) a large-scale cultural knowledge Q&A task. Evaluating 19 LLMs against a fluent-speaker human baseline (n = 30), we find that Gemini 3 Pro Preview achieves 83.3% accuracy on the linguistic task, surpassing the human baseline (78.1%). Proprietary models consistently outperform open-weight systems, and in-language (Gaelic) prompting yields a small but stable advantage (+2.4pp). On the cultural task, leading models exceed 90% accuracy, though most systems perform worse under Gaelic prompting and absolute scores are inflated relative to the manual benchmark. Overall, GaelEval reveals that frontier models achieve above-human performance on several dimensions of Gaelic grammar, demonstrates the effect of Gaelic prompting and shows a consistent performance gap favouring proprietary over open-weight models.

The British National Corpus (BNC) is a 100 million word collection of samples of written and spoken language from a wide range of sources, designed to represent a wide cross-section of British English from the later part of the 20th century, both spoken and written. It is one of the first generation of monolingual, synchronic, general, representative corpora of its size, and led the way for other national corpora. It was created by a consortium of academic partners and publishers, with funding from the Department of Trade and Industry in the UK. This posters reflects on a number of lessons learning in more than thirty years, in terms of corpus representativeness, modes of access to the corpus, licensing, and managing the transition from a contemporary synchronic corpus to a historical corpus.

Currently, research infrastructures are being designed and established in many disciplines since they all suffer from an enormous fragmentation of their resources and tools. In the domain of language resources and tools the CLARIN initiative has been funded since 2008 to overcome many of the integration and interoperability hurdles. CLARIN can build on knowledge and work from many projects that were carried out during the last years and wants to build stable and robust services that can be used by researchers. Here service centres will play an important role that have the potential of being persistent and that adhere to criteria as they have been established by CLARIN. In the last year of the so-called preparatory phase these centres are currently developing four use cases that can demonstrate how the various pillars CLARIN has been working on can be integrated. All four use cases fulfil the criteria of being cross-national.

The paper provides a general introduction to the CLARIN project, a large-scale European research infrastructure project designed to establish an integrated and interoperable infrastructure of language resources and technologies. The goal is to make language resources and technology much more accessible to all researchers working with language material, particularly non-expert users in the Humanities and Social Sciences. CLARIN intends to build a virtual, distributed infrastructure consisting of a federation of trusted digital archives and repositories where language resources and tools are accessible through web services. The CLARIN project consists of 32 partners from 22 countries and is currently engaged in the preparatory phase of developing the infrastructure. The paper describes the objectives of the project in terms of its technical, legal, linguistic and user dimensions.