This week’s AI tip is about: How to imbue large language models (LLMs) like GPT-4 with memory for meaningful conversations
Stateless Nature of LLMs
The first thing to remember is that LLMs are inherently stateless. What this means is that they do not have a built-in capability to remember past interactions or the context in which they occur. Each query to the model is independent, which limits the continuity and cohesiveness in ongoing conversations.
The Need for Conversation Memory
The stateless architecture of LLMs raises a fundamental question: How do we make these models remember? Implementing a memory of the conversation is vital for enhanced user experience, personalized interactions and effective problem-solving.
Techniques for Enabling Memory
There are several techniques to facilitate conversation memory in LLMs. Each comes with its advantages and trade-offs. Here are some popular methods:
- Conversation Buffer Memory
In this approach, the entire conversation is stored, including system messages, user prompts and AI responses. This creates a robust context and allows the AI to make sense of the ongoing dialogue effectively.
- Conversation Window Memory
Rather than storing the entire conversation, this technique saves only the last 'x' messages. This is more efficient in terms of computational resources but may lose essential context in lengthy conversations.
In this method, only the last 'x' number of tokens are stored. Tokens are the basic building blocks of language that models read, like words or subwords. This approach makes it possible to precisely control what the model retains – but it can also be restrictive.
- Conversation Summary Memory
Instead of saving the entire conversation, a summary is created and stored. This is useful for encapsulating the crux of lengthy discussions and can be incredibly efficient, although there is a risk of it losing nuances.
Conclusion
The types of memory mechanisms outlined above are among the most popular, but there are certainly more to explore. Choosing the right type of memory is crucial for a couple of reasons. First, most LLMs have limitations on the number of input tokens they can process. Second, many large language models operate on a pricing model that accounts for both input and output tokens, making efficiency a key consideration.
