LLM Training, Evaluation & RLHF Data Annotation
Contributed to the training and evaluation of large language models through supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Tasks included writing high-quality prompts and reference responses, rating model outputs for accuracy, relevance, safety, and helpfulness, and ranking multiple responses based on detailed evaluation criteria. Identified hallucinations, logical inconsistencies, and policy violations to improve model reliability and alignment. Maintained strict adherence to annotation guidelines and quality benchmarks while working on diverse domains, including technical, business, and general knowledge tasks.