alt Hacker News> The model definitely remembers previous exchanges within the same conversation.
No it doesn't. They get added to its context, and it reads them afresh when answering the next question. That's not remembering.
If your short-term memory completely malfunctioned one day, so you had no ability to remember what was said to you a minute ago, then you would have to find workarounds. For example, you could write down everything someone says to you, then read your notes of the previous exchanges in that conversation in order to continue the conversation. That would be a good way to work around the fact that your short-term memory was broken. And if your notes were invisible to other people and you could read them really fast, then you could even make most people believe that you remembered what they said a minute ago. But you don't actually have a working memory, you're just writing down what they said and re-reading it while coming up with your next response.
That's exactly what LLMs do. That's not memory.
This is really semantics, but I wouldn't call attending to the KV cache re-reading the context.
The model takes in the context, encodes it into a "memory" (the KV cache), and accesses that memory later. That fact doesn't change just because the KV cache grows in size with the context.
I don't know what memory would look like other than an encode-retrieve loop.
Relevant: Transformers are Multi-State RNNs - https://arxiv.org/abs/2401.06104