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cs.CL2025
Nexus: Higher-Order Attention Mechanisms in Transformers
Hanting Chen, Chong Zhu, Kai Han +6
Transformers have achieved significant success across various domains, relying on self-attention to capture dependencies. However, the standard first-order attention mechanism is o…
cs.CV2025
Revealing the Power of Post-Training for Small Language Models via Knowledge Distillation
Miao Rang, Zhenni Bi, Hang Zhou +6
The rapid advancement of large language models (LLMs) has significantly advanced the capabilities of artificial intelligence across various domains. However, their massive scale an…
cs.CL2025
Unshackling Context Length: An Efficient Selective Attention Approach through Query-Key Compression
Haoyu Wang, Tong Teng, Tianyu Guo +4
Handling long-context sequences efficiently remains a significant challenge in large language models (LLMs). Existing methods for token selection in sequence extrapolation either e…