Progress and Limitations of Large Language Models ChatGPT and GPT-4

Large language models have come a long way from the initial stage of GPT-3 to the emergence of ChatGPT and GPT-4, each bringing its own advantages and limitations. ChatGPT, released in November 2022, quickly garnered 100 million monthly users just two months later. This model is fine-tuned from GPT 3.5, improving responsiveness and dialogue capabilities, as well as supporting other tasks such as programming code writing. On the other hand, GPT-4, introduced in March 2023, is a larger model with the ability to perform complex reasoning and achieve human-level performance on various advanced high school exams.

One notable feature of GPT-4 is its multimodal capability, allowing it to take input in the form of text or a combination of text and images. This opens up a new field in information processing capabilities of language models. However, research has also highlighted some limitations of GPT-4. The model does not allow for fine-tuning, reducing its applicability in specialized fields such as marine biology research. Additionally, GPT-4 does not update its knowledge in real-time and sometimes generates inaccurate facts, which is a noteworthy limitation.

Meanwhile, ChatGPT seems to be more useful for conversational tasks and can be utilized as a chatbot or to assist in programming code writing. This demonstrates progress in the application of large language models in everyday life.

Despite these limitations, the development of large language models like ChatGPT and GPT-4 has made significant strides in artificial intelligence. Understanding and overcoming these limitations will help us maximize the potential of these technologies while addressing remaining challenges.

• Large Language Models: Exploring the Deep Power and Challenges of Large Language Models
• ChatGPT: Progress and Limitations of Large Language Models: ChatGPT and GPT-4
• GPT-4: What is GPT-4? How does GPT-4 Compare to GPT-3?
• Advancements: Advancements of Transformer Model and Attention Mechanism in Natural Language Processing
• Constraints: Understanding the Strengths and Limitations of Tokenization and Vectorization in NLP
• AI Applications: Enhancing Transformer Model Performance: In-depth Analysis and Practical Applications
• Multimodal Capability: Exploring Diverse Tokenization Methods in Natural Language Processing
• Fine-Tuning: Training and Inference with Transformers