RLHF Quality Assurance: Hallucination Detection & Logic Refinement
In this project, I performed deep-dive Quality Assurance on a dataset of 500+ LLM (Large Language Model) responses to ensure they met strict 'Gold-Standard' requirements for model training. Key Contributions: Hallucination Mitigation: Identified and corrected factual errors in model outputs by cross-referencing multi-source data. Reasoning Alignment: Audited 'Chain-of-Thought' responses to ensure the step-by-step logic remained consistent from prompt to final answer. Preference Ranking: Evaluated and scored multiple model variations based on helpfulness, honesty, and harmlessness (HHH) criteria. SOP Compliance: Maintained a 98%+ accuracy rate while adhering to complex, evolving annotation guidelines. The Result: The refined dataset provided the model with clearer, more logical training signals, directly reducing the frequency of contradictory or 'vague' AI responses.