Large language models (LLMs) are being improved with human oversight and fixes to address issues such as bias, security vulnerabilities, and unreliable outputs. Various methods are being explored to detect and correct problems with LLMs, including code audits and security patches. The need for human verification and intervention is emphasized to mitigate potential flaws and errors in LLMs.