Multi-Modal Text Evaluation & RLHF Quality Assurance
Performed sentiment analysis, text classification, and named entity recognition (NER) for various data labeling projects, with additional experience in ranking and comparison tasks. Evaluated model responses for factuality, safety, coherence, and verbosity as part of reinforcement learning from human feedback (RLHF). Ensured strict guideline adherence and edge-case identification to maintain high-quality annotated datasets. Specialized in multi-label text categorization and accuracy validation. Applied guideline-driven analysis to diverse text datasets. Conducted model evaluation using RLHF best practices. Ensured high-volume, detail-oriented data verification with a focus on 99% accuracy.