Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect large language models (LLMs) hold promise for bridging such gaps, existing benchmark datasets often fail to capture the cultural context necessary for accurate interpretation. To address this, we introduce MemeBridge, a curated dataset centered on U.S.-originated memes, designed to capture two complementary perspectives: (1) how Chinese participants interpret these memes, and (2) how U.S. participants anticipate how people from other cultures might misunderstand them. Here, context refers to implicit cultural knowledge—background beliefs, norms, and shared assumptions that shape meme comprehension. The dataset was constructed via a multi-stage crowdsourcing pipeline with rigorous validation, including human agreement checks and GPT-based classification verification. Each meme is annotated with sentiment, emotion, cultural significance, and knowledge type, providing rich supervision for downstream tasks. Notably, we observe that the anticipated misunderstandings from U.S. participants are often inaccurate, highlighting the asymmetries in cultural understanding and the challenges of adopting perspectives beyond one's own. This bidirectional framing -- focusing on both expression and perception -- enables more nuanced benchmarking of cross-cultural comprehension. Our probing of multiple LLMs reveals that while models developed in different cultural contexts exhibit partial cross-cultural understanding, they often struggle with sophisticated interpretations. By contrast, fine-tuning with MemeBridge improves model performance, underscoring the value of culturally grounded resources for training and evaluating LLMs in globally diverse settings.

CHI ’25, Yokohama, Japan

The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment.Researchers have recently explored using large language models (LLMs) to generate synthetic datasets as an alternative approach.However, the effectiveness of the LLM-generated synthetic data in supporting model training is inconsistent across different classification tasks.To better understand factors that moderate the effectiveness of the LLMgenerated synthetic data, in this study, we look into how the performance of models trained on these synthetic data may vary with the subjectivity of classification.Our results indicate that subjectivity, at both the task level and instance level, is negatively associated with the performance of the model trained on synthetic data.We conclude by discussing the implications of our work on the potential and limitations of leveraging LLM for synthetic data generation 1 .