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.

Email and chat communication tools are increasingly important for completing daily tasks. Accurate real-time phrase completion can save time and bolster productivity. Modern text prediction algorithms are based on large language models which typically rely on the prior words in a message to predict a completion. We examine how additional contextual signals (from previous messages, time, and subject) affect the performance of a commercial text prediction model. We compare contextual text prediction in chat and email messages from two of the largest commercial platforms Microsoft Teams and Outlook, finding that contextual signals contribute to performance differently between these scenarios. On emails, time context is most beneficial with small relative gains of 2% over baseline. Whereas, in chat scenarios, using a tailored set of previous messages as context yields relative improvements over the baseline between 9.3% and 18.6% across various critical service-oriented text prediction metrics.