Monitoring ChatGPT Drifts Reveals Substantial Behavior Changes Over Time

AI summary: Stanford and UC Berkeley researchers found significant behavioral changes in large language models (LLMs) like GPT-3.5 and GPT-4 within a few months. Performance shifts included a drop in math problem-solving accuracy, reluctance to answer sensitive questions, and a decline in executable code generation. These changes highlight the need for continuous monitoring and testing of LLMs, as unexpected alterations could disrupt downstream workflows. The study underscores the importance of further research to track LLMs’ progress and establish best practices for their stable integration, especially in sensitive domains.
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