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Enhancing Inference Accuracy of Llama LLM using Reversely Computed Dynamic Temporary Weights
  • Qiruo Xin,
  • Quandong Nan
Qiruo Xin
Guiyang Huoxin Software Co., Ltd

Corresponding Author:[email protected]

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Quandong Nan
Guiyang Huoxin Software Co., Ltd
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Abstract

Reversely computed dynamic temporary weights introduce a novel and significant enhancement to the adaptability and accuracy of large language models. By dynamically recalculating the weights of key hidden layers during inference, our methodology significantly improves the model's performance across various natural language processing tasks. Experimental results demonstrated substantial increases in accuracy, response time, and computational efficiency when compared to the baseline performance. The integration of dynamic weights enabled the model to adjust its internal parameters in real-time, resulting in more precise and context-aware predictions. Statistical analysis confirmed the significance of these improvements, providing robust validation for the proposed enhancements. This research not only advances the state-of-the-art in language model optimization but also paves the way for more intelligent and adaptable AI systems. Future work will address computational overhead and explore broader applicability to other neural network architectures.
28 May 2024Submitted to TechRxiv
03 Jun 2024Published in TechRxiv