This paper introduces UNSC-Bench, a benchmark for evaluating Large Language Models (LLMs) in simulating diplomatic decision-making through United Nations Security Council (UNSC) vote prediction. The dataset includes 469 UNSC resolutions from 1947 to 2025, with voting records for the five permanent members (P5) (United States, China, France, Russia, United Kingdom) and translations in four languages. We analyze 26 LLMs, along with thinking variants, across multiple P5 roles and find that (1) without explicit role assignment, models are diplomatically unaligned, defaulting to high yes rates and failing to match any P5 voting pattern, indicating they lack inherent diplomatic identity; (2) model capability (as measured by MMLU-Pro) is strongly correlated with role-playing accuracy; (3) regional models do not outperform others in predicting their home country’s votes; and (4) multilingual evaluation reveals that prompt language impacts model predictions, particularly for minority vote outcomes.

This paper describes the submission of RWTH Aachen University for the De→En parallel corpus filtering task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use several rule-based, heuristic methods to preselect sentence pairs. These sentence pairs are scored with count-based and neural systems as language and translation models. In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic. Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture. A model trained on 10M randomly sampled tokens reaches a performance of 9.2% BLEU on newstest2018. Using our filtering and ranking techniques we achieve 34.8% BLEU.