Self Checking

Self-checking, the ability of a system to verify its own accuracy and reliability, is a burgeoning area of research focusing on improving the trustworthiness of artificial intelligence, particularly large language models (LLMs). Current efforts concentrate on developing efficient algorithms and model architectures, such as fine-tuned LLMs and reinforcement learning techniques like Deep Q-Networks, to enable self-assessment of reasoning processes, fact-checking, and error correction. This research is crucial for enhancing the dependability of AI systems across diverse applications, from conversational question answering to autonomous robotics and hardware design, ultimately contributing to safer and more reliable AI deployments.