KunlunBaize: LLM with Multi-Scale Convolution and Multi-Token Prediction Under TransformerX Framework

2025-02-27

AI Summary

To address critical limitations of large language models—including low computational efficiency, gradient vanishing, and weak modeling capacity for complex semantic interactions—this paper proposes TransformerX, a novel architecture. Methodologically, it introduces a hybrid attention module integrating multi-scale convolution with adaptive activation functions; designs learnable dense residual skip connections to mitigate gradient decay; and incorporates a multi-token joint prediction mechanism to enhance contextual modeling efficiency. Contributions include significantly improved long-range dependency modeling and local structural awareness, 23% higher training stability, 1.8× faster inference speed, and 37% greater parameter utilization efficiency compared to standard Transformers. Extensive experiments demonstrate state-of-the-art performance across diverse language understanding and generation benchmarks. TransformerX thus establishes a new paradigm for designing efficient and robust large-scale language models.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large language models have demonstrated remarkable performance across various tasks, yet they face challenges such as low computational efficiency, gradient vanishing, and difficulties in capturing complex feature interactions. To address these limitations, a novel framework has been proposed. This framework incorporates a learnable dense residual skip connection mechanism, a TransformerX module a transformer based component integrating multiscale convolution and adaptive activation functions and a multitoken prediction interaction module. The learnable dense residual connections enhance information flow and feature capture across layers. Within the TransformerX module, large convolutional kernels aggregate semantic information from extensive text segments, while smaller convolutions focus on local word order and syntactic structures. The adaptive activation function dynamically adjusts its parameters based on the semantic features of the input text, improving the model's ability to handle diverse semantic expressions and complex relationships. The multitoken prediction module boosts data utilization and accelerates inference by predicting multiple future tokens. These components significantly enhance the performance and efficiency of large language models.

Problem

• Improve computational efficiency in large language models
• Address gradient vanishing and feature interaction challenges
• Enhance semantic and syntactic feature capture in text

Innovation

• Learnable dense residual skip connection mechanism
• TransformerX with multiscale convolution and adaptive activation
• Multitoken prediction module for enhanced data utilization