IAM: Efficient Inference through Attention Mapping between Different-scale LLMs (2025.acl-long)
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| Challenge: | Large language models (LLMs) are a challenge due to their internal reasoning processes. |
| Approach: | They propose an algorithm that can optimize attention matrices by performing attention mapping between small and large LLMs. |
| Outcome: | The proposed framework can reduce KV cache usage by 22.1% and accelerate prefill by 15% without sacrificing performance. |
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Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)
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| Challenge: | Existing static compression methods suffer from coarse-grained caching and high I/O overhead. |
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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li
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Yilong Chen, Guoxia Wang, Junyuan Shang, Shiyao Cui, Zhenyu Zhang, Tingwen Liu, Shuohuan Wang, Yu Sun, Dianhai Yu, Hua Wu
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| Challenge: | Existing methods to compress the KV cache of large language models are expensive and limited in their context window and cost. |
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Efficient Inference for Large Language Models –Algorithm, Model, and System (2025.emnlp-tutorials)
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| Challenge: | Inference of LLMs incurs high computational costs, memory access overhead, and memory usage, leading to inefficiencies in terms of latency, throughput, power consumption, and storage. |
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