AI chatbots can generate content effectively in English, but their performance deteriorates in other languages, lacking cultural context. Large language models often resemble knowledgeable outsiders, missing local nuances. For example, a math question involving pie is irrelevant in a culture unfamiliar with it. The creation of culturally aware models requires diverse data in local languages. This challenge is compounded for languages with fewer online resources, necessitating hands-on work by native speakers to curate quality datasets, exemplified by the Thai LLM Typhoon that excelled due to such efforts.
AI chatbots excel in English, but their performance declines in other languages, reflecting a cultural disconnect similar to an outsider's perspective on a region.
In countries like India, culturally significant concepts, such as pie, are often lost on generic AI models, demonstrating a need for localized training data.
Developing accurate AI models in local languages demands extensive effort, including manual curation by native speakers to create quality training datasets.
Typhoon, the Thai LLM, successfully outperformed existing models due to dedicated efforts in curating quality datasets and training from native speakers.
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