Andrei Calazans

The State Of GPT by Andrej

☕️ 2 min read

This talk at Microsoft Build by Andrej Karpathy is a great introduction into Large Language Models (Llms). It clarified multiple doubts about the different available models plus how fine tuning happened.

I suggest you watch the entire talk given how impactful this technology is. But here are a few notes and thoughts I got from the talk.

This is not a summary.

“Base models are not assistants”

At 10:37 Andrej mentions how “Base models are not assistants”. I found this particularly interesting to understand when you compare other models with ChatGTP 4.

Andrej explains that at the base level, LLMs are good at text completion and not assisting. The assistance abilities we see in ChatGTP 4 comes from all the later fine tuning that happens. Thus if you compare a raw base model like Llamma with ChatGTP you feel disappointed.

Assistant training pipeline

The process goes from: Pretraining -> Supervised Finetuning (SFT) -> Reward Modeling (RM) -> Reinforcement Learning (RL).

Each step generates a model. Pretraining is the only step without human input, there is research and methods of removing humans from these other steps.

Base models are still useful in some cases

I found it insightful to learn from Andrej that Base models produce more diverse outputs making it better for scenarios where you want less predictability and more creativity.

You could pair a base model with some prompt engineering (more on this at the bottom) to get desired outcomes.

Explanation of prompt engineering jargon

Since LLms are its core don’t reason and are just text completion engines you can add context and leave a blank space for them to complete it for you. The following jargon is all about the styles of these context.

  • Zero shot: Introduces the context and leave a blank for the Llm.
  • Few shot: Introduces the context and a one or more completed examples before introducing the blank space the Llm to complete.

Narrow down what context to leverage

By default Llms will use their entire memory to complete you prompt, but when you define a character or trait for them as context they will narrow down the memory to use resulting in better assistance.

Constrained Prompting

Similarly to the above, since Lllms are completion engines you can constrained them to produce desired outcome. Like request them to only produce JSON or only fill out certan blank spaces in your document.

LLms don’t know what they don’t know.

Improve Llms Reasoning Throught ReAct

With the help of a strategy the forces the Llms to think, act, and observe its results you can achieve better completions.