Large language models can be inconsistent and confidently incorrect, leading to a loss of trust and making them ineffective for critical tasks like security vulnerability scanning. This article proposes a human-in-the-loop system where expert corrections are systematically fed back into the model. This feedback loop aims to improve accuracy over time, transforming potentially unreliable AI agents into dependable tools by ensuring every output is reviewed and learned from. AI
IMPACT Implementing human-in-the-loop systems can enhance the reliability and trustworthiness of AI agents for critical applications.
RANK_REASON The item is an opinion piece discussing a methodology for improving AI systems, not a direct release or research finding.
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