ChatGPT's response to our 3rd question: "What do I have to learn in order to become a competent user of applications based on large language models?"

Last update: Monday 4/10/23 

On 4/10/23 the editor of this blog posed the question in the title of this note to ChatGPT running its GPT-4 model.  In the editor's opinion, the chatbot's detailed response lays out a realistic learning program; it is not a "hallucination".

Note that this is the editor's second revision of a question he originally posed to ChatGPT on 4/8/23, revised yet again because this third version is a simpler, more effective "prompt" to ChatGPT to produce guidance for obtaining the knowledge and skills that most non-AI experts really want ... :-)

Question

What do I have to learn in order to become a competent user of applications based on large language models?

ChatGPT's response

"To become a competent user of applications based on large language models, you don't necessarily need a deep understanding of their inner workings. Instead, focus on the following areas:

Familiarity with NLP basics:
Understand fundamental concepts in natural language processing, such as tokenization, stemming, and lemmatization. This will help you better comprehend the capabilities and limitations of language model-based applications.

Popular applications: 

Gain experience with commonly used applications of large language models, such as chatbots, content generation, summarization, translation, sentiment analysis, and question-answering systems.

Programming languages and libraries: 

Learn the basics of a programming language like Python, which is widely used in the AI and ML community. Familiarize yourself with popular libraries and tools for interacting with large language models, such as Hugging Face's Transformers, TensorFlow, and PyTorch.

API usage: 

Learn how to use APIs (Application Programming Interfaces) to interact with large language models. Many models, including GPT-3, offer APIs that allow you to access their capabilities without in-depth knowledge of the underlying technology.

Model selection and customization: 

Understand the differences between various language models (e.g., GPT, BERT, RoBERTa) and their use cases. Learn about fine-tuning and transfer learning to adapt pre-trained models for specific tasks.

Performance evaluation: 

Learn how to evaluate the performance of applications based on large language models using appropriate evaluation metrics, such as accuracy, F1 score, or BLEU score.

Ethics and responsible use: 

Develop an understanding of the ethical implications of using large language models and their applications, including potential biases and privacy concerns. Use these applications responsibly and consider their broader societal impact.

Stay up-to-date: 

Follow the latest developments in large language model applications through blogs, online resources, and forums. This will help you stay informed about new tools, techniques, and potential use cases.

By focusing on these areas, you can become a competent user of applications based on large language models without needing to delve into the technical details of their architecture and training processes."

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Links to related notes on this blog:  

• "Useful AI News" -- Updated twice each week
• "How are users who are not AI experts employing large language models, e.g., ChatGPT ???" -- Last update: 3/24/23