CHUNG Po-Long

Contributed to data annotation and evaluation for conversational AI models; improved model accuracy through research and fact-checking; collaborated with cross-functional teams to refine model performance with emphasis on context-awareness and user intent recognition; completed AI evaluation protocol training and data labelling in math and coding domains.

Conducted data annotation and evaluation for conversational AI models, ensuring high-quality training datasets for natural language processing (NLP) applications. Applied research and fact-checking skills to validate AI-generated responses, improving model accuracy and reliability. Collaborated with cross-functional teams to refine AI model performance, focusing on context-awareness and user intent recognition. Completed rigorous training in AI evaluation protocols, earning certifications in language proficiency and preference evaluation. Conducted data labelling and evaluation in math and coding domains, enhancing model accuracy.

Conducted data annotation and evaluation for conversational AI models, ensuring high-quality training datasets for natural language processing (NLP) applications. Applied research and fact-checking skills to validate AI-generated responses, improving model accuracy and reliability. Collaborated with cross-functional teams to refine AI model performance, focusing on context-awareness and user intent recognition. Completed rigorous training in AI evaluation protocols, earning certifications in language proficiency and preference evaluation. Conducted data labelling and evaluation in math and coding domains, enhancing model accuracy.