Text & Sentiment Annotation for LLM Research Studies
Participated in multiple academic and industry research projects focused on improving NLP and LLM systems. Tasks included classifying text, labeling sentiment and emotional tone, rating AI-generated responses for quality, relevance, and safety, and selecting preferred outputs in RLHF-style comparisons. Worked with detailed annotation guidelines, handled ambiguous edge cases, and maintained consistency across large batches of samples (hundreds to thousands of items). Each project included built-in quality controls such as attention checks, gold-standard items, and inter-annotator agreement validation. Consistently met accuracy and reliability thresholds required to remain eligible for advanced tasks. This work contributed to training and evaluating machine learning models used in natural language understanding and generation.