This research introduces a novel concurrent verification framework that detects and corrects factual errors in LLM outputs in real-time, rather than waiting until generation is complete.

• Enables token-by-token verification during the generation process
• Reduces end-to-end latency by up to 71% compared to post-generation verification
• Maintains or improves verification quality through prompt-based verification modules
• Enhances security by immediately preventing misinformation from appearing in outputs

From a security perspective, this approach significantly reduces the risk of AI systems delivering harmful or misleading information, making LLMs safer and more reliable for critical applications.

Real-time Verification and Refinement of Language Model Text Generation