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MemGPT – LLMs with self-editing memory for unbounded context

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Re: MemGPT – LLMs with self-editing memory for unbounded context

#71

Post by sharkjacobs »

lgats wrote: post #60
Der_Einzige wrote: post #28

Earlier quoted context omitted.

ChatGPT degrades precisely because they aren't doing anything special to extend their memory beyond the context length. There are trivial techniques to implement "lossy" memory, such as just average pooling tokens (the same approach used by sentence transformers). Not sure why it's so rare to see this used for condensing a huge amount of context into a prompt. It is effectively "medium" term memory.

https://chat.openai.com/share/e367a1de-c28b-4408-aa3d-2e4b85... Fed chatGPT special numbers, then 3k tokens, then 2k tokens. after that, it was unable to understand any question about the special numbers provided.

On the other hand

https://chat.openai.com/share/8a0675b6-2876-4606-ac79-646391...

Re: MemGPT – LLMs with self-editing memory for unbounded context

#72

Post by codezero »

o11c wrote: post #2

I don't remember the name, but there's already an Esolang that executes its commands unreliably. Through careful program design, you can ensure that a sequence of commands will execute with 99%, 99.9%, etc. reliability.

Sounds a bit like Malbolge but not quite. https://en.m.wikipedia.org/wiki/Malbolge

Re: MemGPT – LLMs with self-editing memory for unbounded context

#73

Post by sandkoan »

visarga wrote: post #65
kristopolous wrote: post #59

Earlier quoted context omitted.

I'm still very much learning this stuff, but I wonder if that's related to the vanishing gradient problem, which seems to be a fundamental aspect of these types of approaches. (Please don't assume that's correct) https://en.wikipedia.org/wiki/Vanishing_gradient_problem

Vanishing gradient was an issue for non-residual deep networks and vanilla RNNs. While the long context memory issues are along sequence dimension, not network depth. The problem could be some kind of instability of attention as it scales above 10k tokens. A recent paper suggests attention mechanism needs a default value (a "sink"), and its absence produces instability. https://arxiv.org/abs/2309.17453 Another paper…

For anyone who's curious, the paper in question, entitled, "Lost in the Middle: How Language Models Use Long Contexts" (https://arxiv.org/abs/2307.03172)

Re: MemGPT – LLMs with self-editing memory for unbounded context

#74

Post by pacjam »

wokwokwok wrote: post #70

> Resursive summarization (Wu et al., 2021b) is a simple way to address overflowing context windows, however, recursive summarization is inherently lossy and eventually leads to large holes in the memory of the system. Yes, it does. > In our experiments ... conversational context is read-only with a special eviction policy (if the queue reaches a certain size, a portion of the front is truncated or compressed via rec…

Thanks for checking out the paper! Just to clarify in case there was any misunderstanding, recursive summarization is just one part of the memory management in MemGPT: as you mentioned, in MemGPT the conversation queue is managed via recursive summarization, just like in prior work (and many chatbot implementations). However there is also a (read/write) "pinned" section of "LLM memory" that's unrelated to recursive summarization, we call this "working context" in the paper. So MemGPT has access to both recursive summaries (generated automatically), as well as working context, which MemGPT actively manages to keep up-to-date.

These are both separate from MemGPT's external context, which is pulled into the conversation queue via function calls. In all our examples, reads from external context are uncompressed (no summarization) and paginated. MemGPT receives a system alert when the queue summarization is triggered, so if MemGPT needs to keep specific details from the conversation queue it can write it to working context before it's erased or summarized.

In the conversational agent examples, working context (no summarization, and separate from the conversation queue) is used to store key facts about the user and agent to maintain consistent conversation. Because the working context is always seen by the LLM, there's no need to retrieve it to see it. In doc QA, working context can be used to keep track of the current task/question and progress towards that task (for complex queries, this helps MemGPT keep track of details like the previous search, previous page request, etc.).

Re: MemGPT – LLMs with self-editing memory for unbounded context

#75

Post by wokwokwok »

pacjam wrote: post #74
wokwokwok wrote: post #70

> Resursive summarization (Wu et al., 2021b) is a simple way to address overflowing context windows, however, recursive summarization is inherently lossy and eventually leads to large holes in the memory of the system. Yes, it does. > In our experiments ... conversational context is read-only with a special eviction policy (if the queue reaches a certain size, a portion of the front is truncated or compressed via rec…

Thanks for checking out the paper! Just to clarify in case there was any misunderstanding, recursive summarization is just one part of the memory management in MemGPT: as you mentioned, in MemGPT the conversation queue is managed via recursive summarization, just like in prior work (and many chatbot implementations). However there is also a (read/write) "pinned" section of "LLM memory" that's unrelated to recursive s…

> Just to clarify in case there was any misunderstanding

I am not confused.

It's good; it solves a specific set of problems with querying large datasets, the same as a vector search would.

...but the various memory zones you've created make absolutely no difference to the fundamental limitation of the LLM context length.

No matter how you swing it, this is just creative prompt engineering. You're packing the context with relevant information; but, if you have too much relevant information, it won't work.

Re: MemGPT – LLMs with self-editing memory for unbounded context

#76

Post by [deleted] »

wokwokwok wrote: post #70

> Resursive summarization (Wu et al., 2021b) is a simple way to address overflowing context windows, however, recursive summarization is inherently lossy and eventually leads to large holes in the memory of the system. Yes, it does. > In our experiments ... conversational context is read-only with a special eviction policy (if the queue reaches a certain size, a portion of the front is truncated or compressed via rec…

[deleted]

Re: MemGPT – LLMs with self-editing memory for unbounded context

#77

Post by totoglazer »

kristopolous wrote: post #68
visarga wrote: post #65

Earlier quoted context omitted.

Vanishing gradient was an issue for non-residual deep networks and vanilla RNNs. While the long context memory issues are along sequence dimension, not network depth. The problem could be some kind of instability of attention as it scales above 10k tokens. A recent paper suggests attention mechanism needs a default value (a "sink"), and its absence produces instability. https://arxiv.org/abs/2309.17453 Another paper…

That's a really recent paper. Do you actually keep up to date with everything? How do you find the time?

Not to denigrate the person you’re responding to, but to add some context: That paper got a decent amount of attention already. Probably one of the more notable in the literature over the last month. Plus compared to the past year everything is slow now.

Re: MemGPT – LLMs with self-editing memory for unbounded context

#78

Post by majestic5762 »

pacjam wrote: post #74
wokwokwok wrote: post #70

> Resursive summarization (Wu et al., 2021b) is a simple way to address overflowing context windows, however, recursive summarization is inherently lossy and eventually leads to large holes in the memory of the system. Yes, it does. > In our experiments ... conversational context is read-only with a special eviction policy (if the queue reaches a certain size, a portion of the front is truncated or compressed via rec…

Thanks for checking out the paper! Just to clarify in case there was any misunderstanding, recursive summarization is just one part of the memory management in MemGPT: as you mentioned, in MemGPT the conversation queue is managed via recursive summarization, just like in prior work (and many chatbot implementations). However there is also a (read/write) "pinned" section of "LLM memory" that's unrelated to recursive s…

We took a similar approach like MemGPT (working memory: summarized conversation with eviction), but our long memory is a graph we can operate on (add/remove/edit nodes & edges). We bring the top_k nodes and their neighbors in the working memory.

Re: MemGPT – LLMs with self-editing memory for unbounded context

#79

Post by visarga »

kristopolous wrote: post #68
visarga wrote: post #65

Earlier quoted context omitted.

Vanishing gradient was an issue for non-residual deep networks and vanilla RNNs. While the long context memory issues are along sequence dimension, not network depth. The problem could be some kind of instability of attention as it scales above 10k tokens. A recent paper suggests attention mechanism needs a default value (a "sink"), and its absence produces instability. https://arxiv.org/abs/2309.17453 Another paper…

That's a really recent paper. Do you actually keep up to date with everything? How do you find the time?

Just reading a couple papers every day, the most interesting ones, and following up on reddit and twitter to get notified what people are talking about. And I am directly interested in long-context LLMs for a work related task.

I have also been dabbling with neural nets (pre-transformer), especially LSTM which have a "residual" connection, the one I was mentioning. That makes gradients better behaved. Schmidhuber tech.

Re: MemGPT – LLMs with self-editing memory for unbounded context

#80

Post by jdthedisciple »

amelius wrote: post #69
kristopolous wrote: post #59

Earlier quoted context omitted.

I'm still very much learning this stuff, but I wonder if that's related to the vanishing gradient problem, which seems to be a fundamental aspect of these types of approaches. (Please don't assume that's correct) https://en.wikipedia.org/wiki/Vanishing_gradient_problem

Regarding the vanishing gradient problem, has anyone tried to train using only a randomly chosen set of independent parameters in each iteration? (Updating only the weights in a small random independent set).

Are you referring to Regularization?

https://www.kaggle.com/code/sid321axn/regularization-techniq...

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