table-of-contents.json

{
    "sample1": {
      "title": "Understanding Large Language Model Architectures",
      "sections": [
        {
          "title": "1. Fundamentals of LLMs",
          "subsections": [
            "1.1 Definition and Key Concepts",
            "1.2 Historical Development of LLMs"
          ]
        },
        {
          "title": "2. Core Architectural Components",
          "subsections": [
            "2.1 Transformer Architecture",
            "2.2 Attention Mechanisms"
          ]
        },
        {
          "title": "3. Advanced LLM Architectures",
          "subsections": [
            "3.1 GPT (Generative Pre-trained Transformer)",
            "3.2 BERT and Its Variants"
          ]
        },
        {
          "title": "4. Training and Optimization Techniques",
          "subsections": [
            "4.1 Pre-training and Fine-tuning",
            "4.2 Scaling Laws and Efficiency"
          ]
        }
      ]
    },
    "sample2": {
      "title": "Innovations in LLM Architecture Design",
      "sections": [
        {
          "title": "1. Efficient Attention Mechanisms",
          "subsections": [
            "1.1 Sparse Attention",
            "1.2 Linear Attention"
          ]
        },
        {
          "title": "2. Parameter-Efficient Fine-tuning",
          "subsections": [
            "2.1 Adapter Layers",
            "2.2 Prompt Tuning"
          ]
        },
        {
          "title": "3. Multimodal LLM Architectures",
          "subsections": [
            "3.1 Vision-Language Models",
            "3.2 Audio-Text Integration"
          ]
        }
      ]
    },
    "sample3": {
      "title": "Evaluating and Improving LLM Architectures",
      "sections": [
        {
          "title": "1. Benchmarking LLM Performance",
          "subsections": [
            "1.1 Language Understanding Metrics",
            "1.2 Generation Quality Assessment"
          ]
        },
        {
          "title": "2. Addressing LLM Limitations",
          "subsections": [
            "2.1 Bias Mitigation Strategies",
            "2.2 Improving Factual Accuracy"
          ]
        },
        {
          "title": "3. Future Directions in LLM Architecture",
          "subsections": [
            "3.1 Neuromorphic Approaches",
            "3.2 Quantum-Inspired Models"
          ]
        }
      ]
    }
  }