GMTW-Ro: a deterministic benchmark for evaluating large language models on grounded Romanian tasks
We introduce Grounded Multilingual Task Worlds for Romanian (GMTW-Ro), a benchmark designed to evaluate whether large language models can reliably follow complex instructions in Romanian, rather than merely produce fluent text. Existing Romanian benchmarks largely rely on multiple-choice formats, answer extraction, or model-based evaluation, which struggle to assess multi-constraint reasoning and structured task completion. GMTW-Ro addresses these limitations through grounded task worlds: fully specified environments in which model outputs are verified via deterministic, programmatic checks. The benchmark spans four task domains—travel planning, calendar scheduling, context-grounded question answering, and dietary menu planning—requiring both a structured JSON plan and a natural-language explanation in Romanian. Evaluation is decomposed into three orthogonal metrics: Understanding (U), measuring constraint adherence and instruction-following; Generation (G), assessing Romanian text quality through diacritic accuracy, language purity, and code-switching absence; and Faithfulness (F), quantifying consistency between generated plans and their explanations. All instances are automatically verified as solvable using backtracking algorithms. We release two curated datasets: a standard benchmark of 500 instances and an adversarial set of 300 instances with heightened constraint complexity, alongside the complete evaluation toolkit and a purpose-built Romanian NLP library. Evaluation of 11 models reveals substantial performance variation (58.6%–90.7%) and exposes a pronounced knowledge–behavior gap, where models with fluent Romanian generation nevertheless fail core reasoning tasks. Most notably, Romanian-finetuned models underperform their base counterparts: RoLlama3.1-8B scores 20.1 percentage points below Llama-3.1-8B, with structured JSON output success dropping from 95 to 44%. These results raise important questions about how current language adaptation pipelines preserve instruction-following and structured reasoning capabilities.