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The cultural landscape of interactions with dialogue agents is a compellingyet relatively unexplored territory. It's clear that various socioculturalaspects -- from communication styles and beliefs to shared metaphors andknowledge -- profoundly impact these interactions. To delve deeper into thisdynamic, we introduce cuDialog, a first-of-its-kind benchmark for dialoguegeneration with a cultural lens. We also develop baseline models capable ofextracting cultural attributes from dialogue exchanges, with the goal ofenhancing the predictive accuracy and quality of dialogue agents. Toeffectively co-learn cultural understanding and multi-turn dialoguepredictions, we propose to incorporate cultural dimensions with dialogueencoding features. Our experimental findings highlight that incorporatingcultural value surveys boosts alignment with references and cultural markers,demonstrating its considerable influence on personalization and dialoguequality. To facilitate further exploration in this exciting domain, we publishour benchmark publicly accessible at https://github.com/yongcaoplus/cuDialog.

Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys