Claims about the grammatical competence of multilingual language models vary sharply with how competence is measured, yet the interaction between evaluation paradigm, post-training, and language resource availability has not been systematically examined. We evaluate base and post-trained models from six families on Mul...
Zhan-Yu Chen, Jaap Jumelet· Proceedings of the 2026 Conf...· 0 citations
This paper describes the approach of the UniBuc team in tackling the SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials. We used SOLAR Instruct, without any fine-tuning, while focusing on input manipulation and tailored prompting. By customizing prompts for individual CTR sections, in b...
Marius Micluta-Campeanu, Claudiu Creanga, Ana-Maria Bucur et al.· 0 citations
Evaluating Retrieval-Augmented Generation (RAG) systems remains a challenge for Low-Resource Languages (LRLs), where standard reference-based metrics fall short. This paper investigates the viability of the "LLM-as-a-Judge" paradigm for Romanian by adapting the Ragas framework using next-generation models (Gemini 2.5 a...
This paper presents a comparative multi-modal analysis of Italian and Romanian brain rot memes, investigating the factors that contribute to its appeal and the linguistic and cultural distinctions between the two versions. To conduct this analysis, we introduce a multi-modal brain rot dataset named CRIB (Collection of...
Anca Dinu, Andra-Maria Florescu, Marius Micluta-Campeanu et al.· 0 citations
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As ever larger language models grow more ubiquitous, it is crucial to consider their environmental impact. Characterised by extreme size and resource use, recent generations of models have been criticised for their voracious appetite for compute, and thus significant carbon footprint. Although reporting of carbon impac...
Imad Lakim, Ebtesam Almazrouei, Ibrahim Abu Alhaol et al.· 0 citations
Korean dialect corpora are available but not redistributable: weights may be released, while reproducing training and evaluation from the underlying data cannot be, and shared construction and evaluation inventories can substantially inflate estimates of synthetic-data recovery.
FACET, the authors' reference-free submission to the WMT26 Automated Translation Quality Evaluation Task, is presented, which decomposes evaluation into Fluency, Accuracy, and Consistency passes and gives each pass only the context its error type requires.
Ahrii Kim, Chanjun Park, Seong-heum Kim· 0 citations
Researchers increasingly use human-derived bias constructs to study Large Language Models (LLMs), including social-cognitive constructs such as implicit bias and stereotype activation, and cognitive biases such as anchoring, framing effects, and confirmation bias. Such approaches offer alternatives to overt bias probes...
Transformer models such as LegalBERT are increasingly used in legal decision support, raising concerns about both fairness and the transparency of model explanations. These properties are usually evaluated separately, leaving open whether a debiasing intervention that changes fairness also changes how faithfully explan...
Yasmina El Kacemi, S. S. M. Ziabari, A. M. Alsahag· 0 citations
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated learning systems, these data samples are often decentralized and Non-Independently Identically Distributed (Non-IID), causing divergence and performance degradation in t...
Dun Zeng, Xiangjing Hu, Shiyu Liu et al.· 0 citations
Byte-level byte-pair encoding (BBPE) tokenizers are attractive for multilingual large language models (LLMs) because they cover all Unicode text. In UTF-8-based BBPE, however, many scripts start from a higher fallback cost than English: when no learned merges can be applied, a multibyte character requires multiple byte...
It is argued that intervention-based robustness tests should become standard for trustworthy clinical speech models and show that high predictive performance and the absence of a significant diagnostic-group difference in a measured acoustic factor are not sufficient for robustness.
Serli Kopar, Alkis Koudounas, R. Rane et al.· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
