As conversational AI systems increasingly engage users through multi-turn dialogue across diverse cultural contexts, cross-cultural competence, the ability to recognize, respect, and adapt to cultural differences, is essential for effective human-AI interaction. Existing evaluations largely rely on single-turn settings, overlooking the complexities of real-world, multi-turn conversations where cultural cues often emerge gradually. To address this gap, we introduce CultureTalk, a benchmark for evaluating multi-turn cultural reasoning in conversational AI. The dataset contains 1,741 culturally grounded dialogues derived from human-verified cultural knowledge resources and evaluates models’ ability to accumulate cultural context across turns and remain robust to topic shifts. Our evaluation reveals substantial performance gaps between single- and multi-turn settings, highlighting current models’ limitations in sustained culturally grounded context understanding. We further show that fine-tuning on augmented single-turn conversational data and inference scaling significantly improve multi-turn cultural reasoning, yielding a 64.7% relative gain in cultural knowledge entailment with a fine-tuned Qwen3-8B model.

We investigate the extent to which verb alternation classes, as described by Levin (1993), are encoded in the embeddings of Large Pre-trained Language Models (PLMs) such as BERT, RoBERTa, ELECTRA, and DeBERTa using selectively constructed diagnostic classifiers for word and sentence-level prediction tasks. We follow and expand upon the experiments of Kann et al. (2019), which aim to probe whether static embeddings encode frame-selectional properties of verbs. At both the word and sentence level, we find that contextual embeddings from PLMs not only outperform non-contextual embeddings, but achieve astonishingly high accuracies on tasks across most alternation classes. Additionally, we find evidence that the middle-to-upper layers of PLMs achieve better performance on average than the lower layers across all probing tasks.