According to God of Prompt on Twitter, leading frontier AI models such as GPT4, Claude, and Gemini demonstrate minimal overfitting when tested on contamination-free datasets, indicating genuine reasoning capabilities. However, as reported by God of Prompt, many mid-tier open-source models exhibit widespread contamination issues across various sizes and versions. This suggests that while top-tier proprietary models maintain high data integrity and robust reasoning skills, open-source alternatives may face significant challenges in ensuring clean training data and preventing overfitting, which could impact their reliability and business adoption.

According to Anthropic (@AnthropicAI), elicitation attacks are effective across various open-source AI models and chemical weapons-related tasks. The analysis reveals that open-source models fine-tuned using frontier model data experience a greater performance boost in these tasks compared to those trained solely on chemistry textbooks or self-generated data. This highlights a significant risk and practical consideration for the AI industry regarding how model fine-tuning sources can influence susceptibility to misuse, offering important insights for businesses and developers working with open-source large language models.