Multilingual Customer Support Automation

Scenario: Global companies require automated customer support systems capable of handling inquiries in multiple languages with high accuracy.

Process: An LLM generates responses to customer inquiries in various languages. Marvin evaluates the appropriateness, cultural sensitivity, and accuracy of these responses, considering the nuances of each language.

Employ a bilingual evaluation understudy (BLEU) score modified for cross-lingual comparisons, BLEUcl , to assess the quality of LLM-generated responses in different languages.

BP is a brevity penalty ensuring response length matches reference length, wn are weights for n-gram precision scores pn. this score is adapted for multilingual comparisons by incorporating language-specific normalization.

Integration: Utilize a cross-lingual scoring algorithm that compares LLM responses with a database of high-quality, multilingual customer service interactions. This approach ensures that feedback accounts for linguistic and cultural nuances, enabling the LLM to improve its multilingual capabilities within the AiGen ecosystem.