Mapping the LLM Landscape: A Cross-Family Survey of Architectures, Alignment Methods, and Benchmark Performance
Problem Statement
The rapid, fragmented growth of LLM families has produced inconsistent reporting on architectures, training strategies, and alignment techniques, making systematic cross-family comparison difficult for researchers and practitioners. Most existing reviews focus narrowly on single models or isolated aspects (e.g., only alignment or only architecture) rather than offering a unified, reproducible comparative framework. This survey addresses that gap by consolidating architectural, training, and safety information into a structured, extensible, and transparent reference.
Key Novelty
- Unified taxonomy spanning architecture, alignment, and benchmark dimensions across 7 major LLM families
- Feature-driven comparative study of over 50 reconstructed LLM architectures enabling apples-to-apples comparison
- Interactive visualization interface paired with an open-source implementation for transparent, reproducible exploration
- Cross-family synthesis linking alignment/safety mechanisms (instruction tuning, RLHF, constitutional AI) to controllability and reliability outcomes
Evaluation Highlights
- Qualitative, structured comparative analysis of training strategies, data curation, and efficiency optimizations across 50+ reconstructed architectures
- Synthesis of reported benchmark performance trends across GPT, LLaMA 2, Gemini, Claude, DeepSeek, Falcon, and Qwen (no new empirical benchmarking conducted)
Signal Assessment
Methodology
- Systematic literature review of proprietary and open-source LLM families covering architecture, training, and alignment publications
- Architectural decomposition analyzing transformer refinements, mixture-of-experts designs, attention optimizations, long-context modeling, and multimodal integration
- Comparative examination of alignment/safety approaches (instruction tuning, RLHF, constitutional frameworks) and their controllability implications
- Reconstruction of 50+ LLM architectures into a standardized feature schema for structured comparison
- Development of an interactive visualization interface and open-source codebase to support transparency and reproducibility
System Components
Structured classification organizing LLMs by architecture, alignment strategy, and application domain across major families.
Systematic review of transformer refinements, MoE paradigms, attention optimizations, long-context techniques, and multimodal integration.
Comparative analysis of instruction tuning, RLHF, and constitutional AI methods and their effects on controllability and reliability.
Feature-driven dataset reconstructing key design details of over 50 LLM variants to enable standardized cross-model comparison.
Tool for exploring architectural, alignment, and performance comparisons across LLM families.
Publicly released codebase supporting transparency and reproducibility of the comparative survey.
Results
| Dimension | Proprietary Models (GPT, Gemini, Claude) | Open-Source Models (LLaMA 2, Falcon, Qwen, DeepSeek) | Key Trade-off Identified |
|---|---|---|---|
| Architecture | Closed transformer refinements, often MoE-based | Documented MoE/dense variants with open weights | Performance optimization vs architectural transparency |
| Alignment method | RLHF plus constitutional AI frameworks | Instruction tuning plus RLHF variants | Safety robustness vs customizability/reproducibility |
| Context length & multimodality | Extended long-context windows, integrated multimodal pipelines | Rapidly converging via open extensions | Capability breadth vs compute/data cost |
| Transparency | Limited disclosure of training data and methods | Greater openness but variable documentation quality | Reproducibility vs competitive/IP concerns |
Key Takeaways
- Offers a single, structured reference for comparing architectural and alignment choices across the dominant proprietary and open-source LLM families
- The open-source taxonomy and visualization tool can accelerate model selection, benchmarking, and design decisions for practitioners building LLM-based systems
- Alignment strategy (RLHF vs constitutional AI vs instruction tuning) is shown to be as consequential as architecture for controllability and safe deployment
- Surfaces open challenges—transparency, computational cost, data governance, and societal impact—that should inform responsible deployment planning
Abstract
Large Language Models (LLMs) have become foundational to modern Artificial Intelligence (AI), enabling advanced reasoning, multimodal understanding, and scalable human-AI interaction across diverse domains. This survey provides a comprehensive review of major proprietary and open-source LLM families, including GPT, LLaMA 2, Gemini, Claude, DeepSeek, Falcon, and Qwen. It systematically examines architectural advancements such as transformer refinements, mixture-of-experts paradigms, attention optimization, long-context modeling, and multimodal integration. The paper further analyzes alignment and safety mechanisms, encompassing instruction tuning, reinforcement learning from human feedback, and constitutional frameworks, and discusses their implications for controllability, reliability, and responsible deployment. Comparative analysis of training strategies, data curation practices, efficiency optimizations, and application settings highlights key trade-offs among scalability, performance, interpretability, and ethical considerations. Beyond synthesis, the survey introduces a structured taxonomy and a feature-driven comparative study of over 50 reconstructed LLM architectures, complemented by an interactive visualization interface and an open-source implementation to support transparency and reproducibility. Finally, it outlines open challenges and future research directions related to transparency, computational cost, data governance, and societal impact, offering a unified reference for researchers and practitioners developing large-scale AI systems.