In this paper, we explore:
- Why LLMs fail in multilingual and cross-cultural applications.
- The impact of linguistic diversity on AI accuracy and usability.
- How cultural nuances, politeness levels, and conversational norms affect AI communication.
- Why low-resource languages are often overlooked in AI training.
- The ethical risks and biases of English-dominated AI models.
- How localized AI training data enhances accuracy, inclusivity, and global reach.
- How DATAmundi helps companies build better AI solutions through multilingual data.
Download the guide to discover how DATAmundi can transform Large Language Models into truly local AI solutions.