Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps
Researchers are exploring novel approaches to enhance the efficiency and effectiveness of attention mechanisms in transformers. Several papers introduce methods to mitigate issues like over-smoothing and computational bottlenecks, particularly in graph transformers and large language models. Techniques include capacity-controlled attention gating, analyzing attention sinks to differentiate between adaptive no-op and broadcast mechanisms, and developing sparse attention strategies for ultra-long contexts. These advancements aim to improve model performance on various benchmarks while reducing computational costs. AI
IMPACT These research papers introduce techniques to improve transformer efficiency and performance, potentially leading to more capable and cost-effective AI models for various applications.
- Hugging Face
- KV cache
- large language models
- Seonghwan Choi
- DFSAttn
- RTPurbo
- RetroAttention
- SimInsert
- arXiv
- LLMs
- PBS-Attn
- DualKV
- Functional Attention
- OLMo2-7B
- FlashAttention
- Attention-FFN Disaggregation (AFD)
- Armv8 CPUs
- Exponentially Decaying Memory
- Dynamic Hierarchical Sparse Attention (DHSA)
- IntAttention
- LLaMA-3.1-8B
- DeepSeek-V3.2
- Qwen
- DeepSeek-V4
- GraphGPS
- SigGate-GT
- E2Former-V2
- FlashMemory-DeepSeek-V4
- ESM2
- FLaG
- GPT-2
- RoBERTa
- ResNet18