According to Anthropic (@AnthropicAI), when general capabilities are controlled for, AI models trained using Selective Gradient Targeted Masking (SGTM) underperform on the undesired 'forget' subset of knowledge compared to models trained with traditional data filtering approaches (source: https://twitter.com/AnthropicAI/status/1998479611945202053). This finding highlights a key difference in knowledge retention and removal strategies for large language models, indicating that data filtering remains more effective for forgetting specific undesirable information. For AI businesses, this result emphasizes the importance of data management techniques in ensuring compliance and customization, especially in sectors where precise knowledge curation is critical.