Transformers

Why Your Transformer Fine-Tune Degrades on the Original Task After Updating

Your transformer model performs well after fine-tuning on a new dataset, but suddenly becomes much worse at the task it originally mastered. This phenomenon, known as catastrophic forgetting, is one of the biggest challenges in continual learning. Learn why it happens and practical strategies

Jul 19, 2026 5m read 👁 1

Attention Sink Tokens: Why the First Few Tokens Skew LLM Outputs

Large Language Models don't distribute attention evenly across a prompt. Researchers have discovered that certain early tokens can act as "attention sinks," attracting disproportionate attention and influencing outputs in unexpected ways. Learn how attention sink tokens work, why they affect model

Jun 30, 2026 5m read 👁 4