Efficient Conceptual Knowledge Removal in Large Language Models: Methods and Evaluations

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

Опубликована: Окт. 8, 2024

The increasing use of deep neural networks has led to models that accumulate vast amounts knowledge from their training data, often retaining outdated or biased information needs be selectively removed. Novel techniques are required efficiently erase specific conceptual these while maintaining overall performance and avoiding computationally expensive re-training processes. This paper introduces a scalable framework for removal through targeted weight modification sparse fine-tuning, demonstrating how representations can isolated erased without significant degradation the model's broader capabilities. methodology achieves high precision in suppression by leveraging probing gradient-based optimization, ensuring minimal disruption general task performance. Extensive experimental evaluations confirm effectiveness proposed approach, highlighting its application scenarios where adaptive model refinement is essential both accuracy ethical integrity. Contributions field include development flexible efficient mechanism erasure, applicable across various architectures, minimizes computational overhead enhancing responsiveness dynamic requirements.

Язык: Английский

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

Enhancements to Large Language Models: Introducing Dynamic Syntactic Insertion for Improved Model Robustness and Generalization

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Окт. 14, 2024

The growing complexity and scale of modern deep learning models have improved the ability to generate understand human language, yet challenges persist in achieving robust generalization syntactic flexibility.Dynamic Syntactic Insertion (DSI) addresses these limitations through novel introduction random variations during finetuning phase, enhancing model's capacity process diverse linguistic structures.Through empirical experiments on GPT-NeoX architecture, significant performance improvements were observed across multiple metrics, including robustness, fluency, accuracy.The DSI-enhanced model consistently outperformed baseline, particularly handling syntactically complex perturbed datasets, demonstrating its adaptability a broader range inputs.Furthermore, incorporation variability led reductions perplexity increased tasks GLUE benchmark, highlighting method's effectiveness.The findings from this study suggest that augmentation techniques, such as DSI, provide promising pathway for improving resilience language environments.

Язык: Английский