A More Experimental Results B Data Settings 7 Conclusion LLMs have emerged as a significant research area in the field of artificial intelligence. However, despite their exceptional performance across various natural language tasks, the practical application of these models is limited by their significant memory overhead and time efficiency.
To address this issue, we propose anchor-based LLMs with the AnSAN technique. Our experiments demonstrate that by sacrificing a marginal 1.5% in precision, our approach saves 99% of keys/values cache memory while simultaneously improving inference speed by up to 3.5 times.
Our methods' application in machine translation showcases their compatibility and flexibility, effectively enhancing memory efficiency for practical use. Our novel approach is practical, straightforward, flexible, and compatible with existing methods, paving the way for further adoption of LLMs in real-world applications.
While our proposed AnLLMs demonstrate significant improvements in memory efficiency and inference acceleration, there are several limitations to consider: Accuracy trade-off: As observed in the experimental results, our method incurs a slight accuracy loss.
#large-language-models #ai-efficiency #machine-translation #memory-management #anchor-based-techniques
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