According to @transformerclrts, the concept of 'interference weights' discussed in the 'Towards Monosemanticity' publication (transformer-circuits.pub/2023/monosemanticity) provides foundational insights into how transformer models handle overlapping representations. The analysis demonstrates that interference weights significantly impact neuron interpretability, with implications for optimizing large language models for clearer feature representation. This research advances practical applications in model debugging, safety, and fine-tuning, offering business opportunities for organizations seeking more transparent and controllable AI systems (source: transformer-circuits.pub/2023/monosemanticity).