How Attention Sinks Keep Language Models Stable
16 days ago
- #AI Research
- #Transformer Models
- #Attention Mechanisms
- Language models fail catastrophically on long conversations when old tokens are removed, producing gibberish.
- Attention sinks are identified as the first few tokens where models dump unused attention due to softmax constraints.
- StreamingLLM solution keeps the first 4 tokens permanently while sliding the window for others, enabling stable processing of 4M+ tokens.
- OpenAI's latest models include attention sink mechanisms, inspired by StreamingLLM research.
- Attention sinks act as computational pressure valves, preventing model collapse when initial tokens are removed.
- Experiments show models can be trained with dedicated sink tokens, improving efficiency and stability.
- Attention sinks are now integrated into major platforms like HuggingFace, NVIDIA TensorRT-LLM, and OpenAI models.
- Research shows attention sinks prevent over-mixing and improve quantization stability in large models.