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ChatGPT Model Card

This model card provides a clear overview of ChatGPT GPT-4 model. It is designed for beginners, developers, and researchers to understand the model’s features, intended uses, limitations, and safety considerations. This helps users make informed decisions and use GPT-4 responsibly.

1. Basic Details​

  • Model Name: GPT-4 (Generative Pre-trained Transformer 4)
  • Version: Initial release (March 2023)
  • Developer: OpenAI
  • License: Proprietary (API and ChatGPT access)
  • Updates: Includes GPT-4 Turbo (Nov 2023), GPT-4o (May 2024), and GPT-4.1 (April 2025)

2. Intended Use​

  • What it’s for: Generating text, summarizing content, translating languages, answering questions, reasoning tasks, and processing images.
  • Good use cases: Education, research support, coding assistance, creative writing, accessibility tools, and image understanding.
  • Not recommended for: Medical or legal advice, high-stakes decisions, or situations needing guaranteed accurate facts.

3. How it Works​

  • Type: Transformer-based large language model
  • Input: Text (up to 32k tokens), images (for multi-input)
  • Output: Text
  • Training method: Predicts the next word in context and is fine-tuned using human feedback (RLHF)
  • Notes: Exact model size, dataset details, and compute requirements are not publicly released

4. Training & Testing​

  • Data sources: Public text, licensed datasets, and text-image pairs for multi-input capabilities
  • Testing: Evaluated using professional exams (bar exam, USMLE), MMLU benchmark, and red-teaming for safety
  • Results highlights:
    • Bar Exam: Top 10% performance
    • USMLE: 20+ points above passing score
    • Consistently better than GPT-3.5 on reasoning and language tasks

5. Performance​

  • Strengths:
    • Advanced reasoning and problem-solving
    • Multilingual capabilities
    • Coding support
    • Handles long documents (up to 32k tokens)
    • Understands text and images
  • Limitations:
    • Can generate incorrect or misleading content (“hallucinations”)
    • Knowledge is limited to data before September 2021
    • Performance may vary across different groups or domains

6. Safety & Ethics​

  • Known risks: Bias in outputs, potential misuse for disinformation, impersonation, or automation
  • Mitigation measures: Human feedback fine-tuning, safety filters, and ongoing monitoring
  • Limitations: Not fully transparent or interpretable in all outputs

7. Reliability & Privacy​

  • Fairness: Audited for bias, but imperfections remain
  • Clarity: Outputs are not fully understandable
  • Robustness: Reliable in trained domains; less stable in new or unusual tasks
  • Privacy: Should not be used for sensitive or personal data

8. References​