LexNeo-Bench: Probing LLM Knowledge of Lexical Borrowing in Luxembourgish via Knowledge-Graph Prompting
Researchers introduce LexNeo-Bench, a 3,050-instance benchmark for evaluating LLM performance on lexical borrowing classification and neology detection in Luxembourgish, a low-resource contact language. Three multilingual LLMs are tested across 34 prompt configurations; without external context, models perform near chance on borrowing classification (25–35%). Injecting instance-specific subgraphs from a linguistic knowledge graph raises accuracy to 71–81% and largely closes the gap between small and large models, though neology detection remains difficult. The study highlights the value of lexicon-aware, structured prompting for low-resource multilingual evaluation.
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LoSoNA benchmark evaluates LLM adaptation to implicit local social norms in group chats
Researchers introduce LoSoNA, a benchmark for testing whether LLM-based agents can infer and adapt to unstated local conversational norms in multi-party chat scenarios. Each scenario presents a group-chat transcript where non-subject participants implicitly demonstrate a hidden norm, followed by an elicitor turn. Eight frontier and open-weight models are evaluated under four prompting conditions; naive prompting performs poorly for most models, while explicit norm-aware prompting yields uneven gains—Gemini 3.1 Pro reaches 84.2% and Claude Fable 5 reaches 81.6%. The work contributes to growing interest in evaluating LLM social and pragmatic capabilities beyond factual or reasoning tasks.
Phun-Bench: A Chinese benchmark for evaluating LLM phonological understanding
Researchers introduce Phun-Bench, a purpose-built benchmark for evaluating LLMs on phonological understanding in Chinese across three dimensions: Homophony, Rhyme, and Phonetic Similarity. The benchmark is designed to avoid rote-memorization shortcuts that plague existing phonological evals. Results show LLMs can recall correct pronunciations but fail to apply phonological knowledge flexibly as human speakers do, and the authors propose a hypothesis about the underlying mechanism of LLM phonological 'perception'.
Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study
This paper systematically investigates strategies for extending LLM-based automatic evaluation (LLMs-as-a-Judge) to multilingual settings, covering high-, mid-, and low-resource languages (English, Spanish, Basque). The authors compare instruction translation, monolingual vs. multilingual supervision, and model size, finding that fine-tuned smaller models can match proprietary models when in-domain data is available, while zero-shot larger models are preferable out-of-domain. Two meta-evaluation datasets are extended to Spanish and Basque, and all data and code are publicly released.
FilBench: Benchmarking LLM Capabilities in Filipino Language
FilBench is a new benchmark introduced to evaluate large language models on their ability to understand and generate Filipino. The benchmark targets a historically underrepresented language in NLP evaluation suites, assessing both comprehension and generation tasks. This work addresses gaps in multilingual LLM evaluation coverage, particularly for Southeast Asian languages.
Supervised vs. in-context learning for Turkish multiword expression classification
A new arXiv paper evaluates Turkish idiomatic light verb construction (LVC) detection as a binary classification task, comparing a supervised BERTurk baseline against three instruction-tuned LLMs under zero-shot, one-shot, and few-shot prompting. Results show LLMs have very low LVC recall in zero-shot but improve substantially with demonstrations, though one-shot prompting can introduce strong model-specific biases. The supervised baseline remains competitive, while carefully constructed few-shot prompts allow GPT-OSS-20B and Qwen 2.5-14B to match or exceed it. The study highlights significant prompt sensitivity in Turkish metalinguistic classification tasks.
BenCzechMark: A Benchmark for Evaluating LLM Czech Language Understanding
BenCzechMark is a new evaluation benchmark designed to assess large language model performance on Czech language tasks. The benchmark addresses the gap in non-English language evaluation, providing a structured way to measure LLM capabilities in Czech across multiple task types. Published on Hugging Face, it contributes to the growing ecosystem of multilingual and language-specific benchmarks.
Study finds local languages provide better cultural knowledge access in LLMs once proficiency is controlled
A new arXiv paper introduces a controlled evaluation framework to disentangle language proficiency from culture-specific knowledge access in LLMs. Using real-world cultural questions across 13 locales and ~80 models, the authors apply item response theory to show that while English dominates on culture-agnostic questions, local languages yield a consistent knowledge-access advantage on culture-specific questions once proficiency differences are factored out. The finding challenges the common interpretation that weaker local-language accuracy implies weaker cultural knowledge, and has implications for how multilingual and regionally-aligned models are evaluated.
Benchmarking Local LLMs for Confidential Translation Workflows
This paper evaluates locally runnable LLMs (via Ollama) for offline, privacy-constrained translation workflows targeting freelance translators and smaller language service providers. The authors expand their Reeve Foundation corpus to include German and Simplified Chinese, then benchmark local models across four language directions against commercial NMTs (DeepL, Baidu), a frontier LLM (GPT-5.2), and professional local NMT systems. Results show substantial performance variation by language direction and model size, with the best local LLMs matching or exceeding local NMT systems and the frontier LLM, though falling short of top commercial NMTs. The study supports the viability of local LLMs for confidentiality-sensitive translation use cases.