The field of artificial intelligence is moving towards greater cultural awareness and adaptation. Recent research has highlighted the importance of addressing cultural biases in language models and image-generating AI. A key direction is the development of frameworks and resources that can enhance cultural value alignment and provide more balanced coverage of diverse cultural knowledge. Noteworthy papers include:

• Cultural Learning-Based Culture Adaptation of Language Models, which presents a novel framework for enhancing LLM alignment with cultural values.
• CARE: Aligning Language Models for Regional Cultural Awareness, which introduces a multilingual resource for improving LMs across various model families and sizes.
• NativQA Framework: Enabling LLMs with Native, Local, and Everyday Knowledge, which proposes a framework for constructing large-scale, culturally and regionally aligned QA datasets in native languages.