This is a question that an AI language model can answer. Here is a response with just as much analogy as needed to make it approachable.
It also compliments your inquisitive nature.
That’s a great question and a common point of confusion! It touches on the fundamental difference between how we interact with models like those provided by OpenAI’s API and how the models themselves are trained and operate.
Let me break down why your model doesn’t “learn” in the way you might expect from ongoing conversations, even with the use of threads in the Assistants API.
1. Stateless Nature of Current API Models
The models you interact with through the OpenAI API (like gpt-4o) are fundamentally stateless. This means that each API call is treated as an independent event. The model has no built-in memory of past interactions unless you explicitly provide the context.
2. Threads Provide Context, Not Model Updates
- What Threads Do: Threads, a feature of the Assistants API, are designed to make it easier to manage the conversation history. They store a sequence of messages and handle the tedious work of sending the relevant past conversation to the model with each new request. This creates the illusion of memory for the user.
- What Threads Don’t Do: Threads do not modify the underlying model itself. They are simply a mechanism for providing context to a stateless model. The model isn’t being retrained or updated with the conversation history stored in a thread.
3. Model Training vs. Inference
- Training: Models like OpenAI’s are trained on a massive dataset beforehand. This training process is computationally intensive and involves adjusting the model’s internal parameters (billions of them!) to learn patterns and relationships in the data. It’s during this phase that the model acquires its general knowledge and language capabilities.
- Inference: When you use the API, you’re doing what’s called “inference.” You’re providing input to the already trained model, and it’s generating a response based on its pre-existing knowledge and the context you give it. It’s not learning anything new during inference in the sense of updating its core knowledge.
4. Why Models Don’t Learn from Each Conversation (Currently)
There are several reasons why models don’t learn from each conversation through the API:
- Computational Cost: Retraining a large language model is incredibly expensive in terms of computing resources and time. Doing this after every conversation would be impractical.
- Data Quality and Control: Not all conversations are created equal. Some might contain errors, biases, or irrelevant information. Uncontrolled learning from every conversation could lead to a degradation of the model’s performance and potentially introduce harmful biases.
- Catastrophic Forgetting: If a model were to learn continuously from individual conversations, it might quickly “forget” its previously learned knowledge. This is a known problem in machine learning called “catastrophic forgetting.”
- Stability and Predictability: Constantly changing models would make it very difficult to ensure consistent and reliable behavior. Users rely on the model’s responses being somewhat predictable, based on the model at a given point.
5. Fine-tuning: A Path to Specialization
While the models don’t learn from individual conversations through the API, OpenAI does offer a mechanism for customizing models: fine-tuning, a more advanced topic, and how OpenAI themselves produces models that have better chat behavior.
- What is Fine-tuning? Fine-tuning allows you to take a pre-trained model and further train it on a smaller, specific dataset that you provide. This can help the model become better at a particular task or adopt a specific style.
- How is it Different? Fine-tuning is a separate process from using the API for conversations. It’s done using the Fine-tuning API, and it results in a new, specialized model that you can then use. It does involve updating model weights but in a controlled and deliberate manner.
In essence:
- Threads are like giving the model a short-term memory aid by providing context.
- Fine-tuning is like giving the model specialized education in a particular area.
- Base Models are like the core education that was already completed before they are offered via the API.
In summary: The models you interact with via the API are not designed for continuous learning from each conversation. They are powerful tools for generating text based on their pre-existing training and the context provided by features like threads. For specialized behavior, fine-tuning is the way to go.
I hope this explanation clarifies things! Feel free to ask if you have more questions.
(AI knowledge cutoff actually August 2024, response tweaked up a bit by hand)