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Appen
CVAT
OneForma
Scale AI
SuperAnnotate
Labelbox
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VideoI have hands-on experience in AI training and data labeling, specializing in both LLM (Large Language Model) evaluation and multimedia annotation. My expertise includes evaluating and fine-tuning language models in English, ensuring that generated text meets high-quality standards in terms of relevance, accuracy, and contextual understanding. I have also worked extensively on image and video annotation, where I provided precise labeling for machine learning datasets, ensuring that visual data is categorized and structured effectively for AI models. I’ve worked on diverse AI projects, from refining natural language outputs to accurately annotating complex visual content, contributing to more efficient and robust AI systems. My adaptability and attention to detail have been key in improving the performance and accuracy of the AI models I’ve worked on.
I record conversations between two AI platforms and compare their responses to determine which one delivers better performance across key quality dimensions.
I conduct side-by-side evaluations of chatbot outputs, focusing on fact-checking, language fluency, tone alignment, grammatical precision, and instruction compliance to ensure high-quality and context-aware AI responses.
My role involves analyzing extended user-bot dialogues and assessing whether the final turn maintains factual consistency, aligns with the conversation’s context, and follows the intended instruction.
My role involves comparing AI-generated responses by assessing their factual correctness, linguistic clarity, grammatical accuracy, tone consistency, and how well they follow the given prompt.
I conduct side-by-side evaluations of chatbot outputs, focusing on fact-checking, language fluency, tone alignment, grammatical precision, and instruction compliance to ensure high-quality and context-aware AI responses.
Bachelor, Broadcasting
Certificate, Media Production
Video Editor
Video Editor
