Culturally Adaptive Explainable LLM Assessment for Multilingual Information Disorder: A Human-in-the-Loop Approach
📰 ArXiv cs.AI
A human-in-the-loop approach is proposed for culturally adaptive explainable LLM assessment to address multilingual information disorder
Action Steps
- Develop a multilingual dataset like InDor to train and evaluate LLMs
- Implement a human-in-the-loop approach to provide culturally sensitive annotations and feedback
- Fine-tune LLMs using the annotated dataset to improve their performance in explaining manipulated news
- Evaluate the performance of LLMs using metrics that account for cultural and linguistic context
Who Needs to Know This
AI engineers, data scientists, and researchers on a team can benefit from this approach to improve the performance of LLMs in multilingual settings and address information disorder
Key Insight
💡 Current LLMs are often monocultural and English-centric, overlooking localized framing and cultural context
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🚨 Culturally adaptive LLMs can help mitigate information disorder in multilingual settings 🌎
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