LLM-BASED TRANSFORMER FRAMEWORKS FOR SCALABLE AND TRUSTWORTHY INTELLIGENT SYSTEMS

Abstract

The use of large language models in real intelligent systems has two inherent limitations: computational scalability to scale to efficient inference and operational reliability to be able to be deployed. In this paper, the proposal is based on the Scalable Trustworthy Transformer (ST-Transformer), a new framework that simultaneously addresses both purposes with combined architectural innovations. Our approach consists of a Dynamic Sparse Attention mechanism, which reduces the quadratic to near-linear complexity, and an Adaptive Mixture-of-Experts layer, which optimizes the computing resources according to the complexity of inputs to optimization. The framework is built on a multi-modal trustworthiness core that offers real-time uncertainty measurements and bias reduction in embedding, attention, and output layers. Large-scale analysis shows that ST-Transformer can be 2.2x faster with 43% less memory saved, as compared to 86.1% GLUE accuracy. The structure demonstrates 12.4x acceleration of 16K length sequences and decreased energy use by 44 percent. In trustworthiness, it scores 0.031 Expected Calibration Error (63 improvement score) and 67.8 (reduce bias score) across demographic attributes. With these developments, the optimization of scalability and reliability is synergistic and thus allows the next generation of intelligent systems that are computationally efficient and operationally reliable to be deployed in the enterprise.