Deep Dive into Large Language Models: A Comprehensive Technical Overview from Architectural Principles to Efficient Fine-Tuning

Based on a SECOMPP academic presentation and GitHub open-source project, this article systematically organizes the complete technical system of large language models (LLMs), covering core content such as neural network architectures, decoding sampling algorithms, pre-training data engineering, and parameter-efficient fine-tuning (LoRA), helping readers build a comprehensive understanding of modern generative AI.

The Importance of LLMs

LLMs have profoundly changed the way technology interacts, with application scenarios including intelligent writing, code generation, chatbots, etc. Behind their capabilities lies a complex engineering and technical system.

Sources of Materials

• Original Authors: João Gabriel de Morais Bezerra, Daniel Henrique Peres Servejeira
• Source Platform: GitHub (Project Link: https://github.com/DanielServejeira/LLM-presentation)
• Release Time: June 2026, License Agreement: MIT License This article is organized based on the presentation materials shown at SECOMPP (São Paulo State University Computing Academic Event).

Mainstream LLM architectures are divided into three categories:

1. Encoder Architecture (e.g., BERT): Bidirectional attention, suitable for understanding tasks (text classification, sentiment analysis, etc.).
2. Decoder Architecture (e.g., GPT series): Autoregressive generation, suitable for text generation tasks (continuation, code generation, etc.).
3. Hybrid Architecture (e.g., T5, BART): Combines encoding understanding and decoding generation capabilities, applicable to translation, summarization, question answering, etc.

Unified Task Paradigm

Almost all NLP tasks can be converted into sequence prediction: tasks are transformed into conditional generation through prompt design. For example:

• Sentiment Analysis: Input "This movie is amazing. sentiment: " → Output "positive"
• Text Summarization: Input "Original Text: [Article] Summary: " → Generate summary

In-Context Learning

Models quickly adapt to new tasks with a small number of examples without updating parameters, which is the foundation of prompt engineering.

Sampling algorithms affect generation quality and diversity:

1. Temperature Adjustment: Low temperature (T→0) leads to conservative and deterministic results; high temperature (T→∞) leads to diverse and creative results.
2. Top-k Sampling: Select from the k words with the highest probability, balancing quality and diversity.
3. Top-p Sampling: Adaptively select a set of words with cumulative probability reaching p, often used in combination with Top-k and temperature.

Self-Supervised Pre-Training

No manual annotation is required; the goal is language modeling (predicting the next word) to minimize cross-entropy loss.

Large-Scale Datasets

• C4: Hundreds of GB of web text cleaned from Common Crawl
• The Pile: 800GB of diverse text (books, code, papers, etc.) Data cleaning and deduplication are key links.

Scaling Laws

Model performance has a power-law relationship with the number of parameters, data volume, and computation volume; scaling up can stably improve performance.

LoRA Principles

During fine-tuning, the weight update matrix has low-rank characteristics. Introduce low-rank matrices A (r×k) and B (d×r, r is much smaller than d and k). The update formula is W' = W + BA. Only A and B are trained, and the original weights are frozen.

LoRA Advantages

• Memory Saving: Trainable parameters are reduced to less than 1/1000
• Training Acceleration: Reduced backpropagation computation
• Flexible Deployment: Share the base model, only need to store lightweight adapters
• Performance close to full fine-tuning, becoming an industry standard (used by ChatGPT, Claude, etc.).

Model Evaluation

• Perplexity: Measures prediction ability, lower values are better
• Downstream Task Accuracy: Performance on specific tasks
• Human Evaluation: The most reliable method for generation tasks

Socio-Technical Risks

• Hallucinations: Generate incorrect content
• Copyright Disputes: Training data contains copyrighted works
• Harmful Content: Biased, discriminatory information
• Environmental Impact: High energy consumption

Learning Recommendations

Start with practice: Experiment with open-source models, read the latest papers, participate in community discussions, and combine theory and practice to master the essence of the technology.