Natasha Kogut

and 4 more

Latent feature transformation introduces a novel framework for enhancing linguistic performance through the direct manipulation of latent space dynamics within pre-trained language models. By focusing on recalibrating representational hierarchies, the methodology achieves improved generalization and task-specific adaptability without requiring extensive retraining or architectural redesigns. Experimental results highlight substantial performance improvements across sentiment analysis, machine translation, and summarization tasks, showcasing the model's ability to align with diverse linguistic challenges. The framework leverages differentiable mappings to systematically adjust latent feature distributions, preserving semantic integrity while optimizing task relevance. Quantitative analysis reveals enhanced semantic coherence and error reduction across complex linguistic categories, affirming the robustness of the transformation. Computational efficiency gains, reflected in reduced inference latency and memory usage, demonstrate the practicality of the approach for deployment in resource-constrained environments. Comparative evaluations position latent feature manipulation as a scalable alternative to traditional methods reliant on parameter scaling or extensive fine-tuning. The integration of this framework into pre-trained architectures demonstrates its capacity to enhance interpretability and precision across emergent tasks. Comprehensive experimental validation, supported through rigorous evaluation metrics and diverse datasets, substantiates the technical and operational feasibility of the methodology. Analysis of attention patterns reveals improved alignment with task-critical tokens, further highlighting the framework's potential to refine linguistic outputs. These findings establish latent transformations as a critical avenue for advancing the adaptability and efficiency of large-scale language models in both theoretical and applied domains.