Transformers have been the backbone of the AI community as it pertains to natural language processing, code generation, and multimodal reasoning. The article offered an analysis of the architectural standing of four major LLMs OpenAI’s Chat GPT, Google’s Gemini, GitHub Copilot, and DeepSeek premising on how, the transformer-based frameworks in developing, have advanced to cover the modern AI requirements. The paper presents the most crucial architectural upgrades such as Mixture-of-Experts (MoE) routing, rotary, and sparse attention, unified multimodal embeddings, and long-context capabilities. The approach of this research was through a step-by step breakdown of various models but the focus was on the architectures indicating how they were transformed to support different tasks. Each model was refitted differently to be able to carry out its core activity: ChatGPT was made to be alignment-aware, Gemini was aimed at the fastest possible scaling speed in terms of latency, Copilot was targeted at real-time code generation, and DeepSeek was changed for work on symbolic math and bilingual generation. Besides, the study elaborated on utilization of the newest approaches like retrieval augmented generation (RAG), external memory, and cross-modal self-attention that improve understanding, mitigate hallucinations, and enable learning of long context. The analysis revealed that LLMs are being radically modified by the aspects of the model that can be reconfigured according to the specific tasks they have to perform. This signifies that the way forward for the greater part of LLMs has shifted from scale to architecture, via the implementation of features such as through the proposed innovations and structural adjustments across the layers of the models.
Paper
Full text
Next-Gen AI: Architectural Insights into Large Language Models
Semantic Scholar · 2025
Abstract
Transformers have been the backbone of the AI community as it pertains to natural language processing, code generation, and multimodal reasoning. The article offered an analysis of the architectural standing of four major LLMs OpenAI’s Chat GPT, Google’s Gemini, GitHub Copilot, and DeepSeek premising on how, the transformer-based frameworks in developing, have advanced to cover the modern AI requirements. The paper presents the most crucial architectural upgrades such as Mixture-of-Experts (MoE) routing, rotary, and sparse attention, unified multimodal embeddings, and long-context capabilities. The approach of this research was through a step-by step breakdown of various models but the focus was on the architectures indicating how they were transformed to support different tasks. Each model was refitted differently to be able to carry out its core activity: ChatGPT was made to be alignment-aware, Gemini was aimed at the fastest possible scaling speed in terms of latency, Copilot was targeted at real-time code generation, and DeepSeek was changed for work on symbolic math and bilingual generation. Besides, the study elaborated on utilization of the newest approaches like retrieval augmented generation (RAG), external memory, and cross-modal self-attention that improve understanding, mitigate hallucinations, and enable learning of long context. The analysis revealed that LLMs are being radically modified by the aspects of the model that can be reconfigured according to the specific tasks they have to perform. This signifies that the way forward for the greater part of LLMs has shifted from scale to architecture, via the implementation of features such as through the proposed innovations and structural adjustments across the layers of the models.