Optimizing Large Language Models with Multi-Degree Low-Rank Approximations

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 27, 2024

The increasing computational demands and resource requirements of advanced neural network models have created a growing need for efficient methods to enhance their scalability deployment, particularly in environments with limited hardware capabilities. Addressing this challenge, the novel application multi-degree low-rank approximations provides significant breakthrough, enabling substantial reductions memory usage costs while preserving high levels performance. Experiments conducted on Mistral model demonstrated that approach can effectively balance trade-offs between complexity accuracy, achieving reduced perplexity improved classification performance across range tasks. use varying degrees rank reduction allowed tailored optimization, enhancing model's adaptability different task operational environments. findings suggest are not only viable solution optimizing large-scale networks but also versatile tool extending applicability sophisticated language resource-constrained settings. This opens up new possibilities deployment processing capabilities real-time applications, mobile devices, other platforms where efficiency is critical.

Язык: Английский

Geometric Problem-Solving in Large Language Models through Rule-Based Alignment and Calibration

Опубликована: Авг. 30, 2024

Geometric problem-solving remains a challenging area for artificial intelligence due to the necessity precise rule application and spatial reasoning.A novel approach is introduced in this research that incorporates rule-based alignment within architecture of an open-source language model, Llama, enhance its geometric reasoning capabilities.Through embedding explicit rules into model's neural network, modified Llama demonstrates improved accuracy efficiency solving wide range problems, from basic shape recognition complex theorem application.The study employs geometry-focused curriculum training, which progressively increases complexity, enabling model develop robust understanding principles.Experimental results, compared with baseline reveal significant improvements accuracy, consistency, adherence rules, highlighting efficacy strategy.The findings suggest integrating structured knowledge models can lead substantial advancements their ability perform specialized mathematical tasks, thereby broadening scope applications scientific technical domains.

Язык: Английский