CultureConverse: A Multilingual Multi-turn Simulation Harness for Culturally Grounded Assistance in East and Southeast Asia

A new evaluation framework, CultureConverse, has been developed to assess large language models (LLMs) in culturally grounded, multi-turn conversational scenarios. Unlike previous methods that focused on single-turn factual recall, CultureConverse simulates user interactions across 10 East and Southeast Asian regions, incorporating 58 subgroup identities and 7 domains. It evaluates an assistant's ability to provide practical help and infer cultural constraints from partial information, resulting in a dataset comprising over 14,000 benchmark episodes and over 274,000 oracle-guided episodes.

This development is crucial for advancing the capability and ethical deployment of AI assistants, particularly in diverse global markets. By providing a robust method to evaluate cultural sensitivity and practical assistance, it directly impacts the trustworthiness and effectiveness of AI systems in real-world, human-centric applications, reducing risks associated with cultural misunderstandings or insensitivity.

Existing LLM cultural evaluations are often limited to single-turn factual recall via multiple-choice questions.

Forward consideration, not a verified fact.