Think of LLM as an operating system, it has unlimited 'virtual' context, Berkeley's new work has received 1.7k stars

In recent years, large language models (LLMs) and their underlying transformer architecture have become the cornerstone of conversational AI and have spawned a wide range of consumer and enterprise applications. Despite considerable progress, the fixed-length context window used by LLM greatly limits the applicability to long conversation or long document reasoning. Even for the most widely used open source LLMs, their maximum input length only allows support of a few dozen message replies or short document inference.

At the same time, limited by the self-attention mechanism of the transformer architecture, simply extending the context length of the transformer will also cause the calculation time and memory cost to increase exponentially, which makes a new long context architecture urgent. research topic.

However, even if we can overcome the computational challenges of context scaling, recent research has shown that long-context models struggle to effectively utilize the additional context.

How to solve this? Given the massive resources required to train SOTA LLM and the apparent diminishing returns of context scaling, we urgently need alternative techniques that support long contexts. Researchers at the University of California, Berkeley, have made new progress in this regard.

In this article, researchers explore how to provide the illusion of infinite context while continuing to use a fixed context model. Their approach borrows ideas from virtual memory paging, enabling applications to process data sets that far exceed available memory.

Based on this idea, researchers took advantage of the latest advances in LLM agent function calling capabilities to design an OS-inspired LLM system for virtual context management - MemGPT.

Paper homepage: https://memgpt.ai/

arXiv address: https://arxiv.org/pdf/2310.08560.pdf

The project has been open sourced and has gained 1.7k stars on GitHub.

GitHub address: https://github.com/cpacker/MemGPT

Method Overview

The The research draws inspiration from the hierarchical memory management of traditional operating systems to efficiently "page" information in and out between context windows (similar to "main memory" in operating systems) and external storage. MemGPT is responsible for managing the control flow between memory, LLM processing module and users. This design allows for iterative context modification during a single task, allowing the agent to more efficiently utilize its limited context window.

MemGPT treats the context window as a restricted memory resource and designs a hierarchical structure for LLM similar to hierarchical memory in traditional operating systems (Patterson et al., 1988). In order to provide longer context length, this research allows LLM to manage content placed in its context window through "LLM OS" - MemGPT. MemGPT enables LLM to retrieve relevant historical data that is lost in context, similar to page faults in operating systems. Additionally, agents can iteratively modify the contents of a single task context window, just as a process can repeatedly access virtual memory.

MemGPT enables LLM to handle unbounded contexts when the context window is limited. The components of MemGPT are shown in Figure 1 below.

MemGPT coordinates the movement of data between the main context (content in the context window) and external contexts through function calls. MemGPT updates and retrieves autonomously based on the current context.

It is worth noting that the context window needs to use a warning token to identify its restrictions, as shown in Figure 3 below:

Experiments and results

In the experimental part, the researchers evaluated MemGPT in two long context domains, namely conversational agents and document processing. For conversational agents, they extended the existing multi-session chat dataset (Xu et al. (2021)) and introduced two new conversation tasks to evaluate the agent's ability to retain knowledge in long conversations. For document analysis, they benchmark MemGPT on tasks proposed by Liu et al. (2023a), including question answering and key-value retrieval of long documents.

MemGPT for Conversational Agents

When talking to the user, the agent must meet the following two key criteria.

• The first is consistency, that is, the agent should maintain the continuity of the conversation, and the new facts, references, and events provided should be consistent with the previous statements of the user and the agent.

• The second is participation, that is, the agent should use the user's long-term knowledge to personalize the response. Referring to previous conversations can make the conversation more natural and engaging.

Therefore, the researchers evaluated MemGPT based on these two criteria:

• Can MemGPT leverage its memory to improve conversational consistency? Can you remember relevant facts, quotes, events from past interactions to maintain coherence?

• MemGPT Is it possible to use memory to generate more engaging conversations? Spontaneously merge remote user information to personalize information?

Regarding the data set used, the researchers evaluated and compared MemGPT and the fixed context baseline model on the multi-session chat (MSC) proposed by Xu et al. (2021).

First come to consistency evaluation. The researchers introduced a deep memory retrieval (DMR) task based on the MSC dataset to test the consistency of the conversational agent. In DMR, a user poses a question to a conversational agent, and the question explicitly references a previous conversation, with the expectation that the answer range will be very narrow. For details, please refer to the example in Figure 5 below.

MemGPT utilizes memory to maintain consistency. Table 2 below shows the performance comparison of MemGPT against fixed memory baseline models, including GPT-3.5 and GPT-4.

It can be seen that MemGPT is significantly better than GPT-3.5 and GPT-4 in terms of LLM judgment accuracy and ROUGE-L score. MemGPT can use Recall Memory to query past conversation history to answer DMR questions, rather than relying on recursive summarization to expand context.

Then in the "conversation starter" task, the researchers assessed the agent's ability to extract engaging messages from the accumulated knowledge of previous conversations and deliver them to the user.

The researchers show the CSIM scores of MemGPT’s opening remarks in Table 3 below. The results show that MemGPT is able to produce engaging intros that perform as well as or better than human handwritten intros. It is also observed that MemGPT tends to produce openings that are longer and cover more character information than the human baseline. Figure 6 below is an example.

MemGPT for document analysis

To evaluate MemGPT’s ability to analyze documents, We benchmarked MemGPT and a fixed-context baseline model on the Liu et al. (2023a) retriever-reader document QA task.

The results show that MemGPT is able to efficiently make multiple calls to the retriever by querying the archive storage, allowing it to scale to larger effective context lengths. MemGPT actively retrieves documents from the archive store and can iteratively page through the results so that the total number of documents available to it is no longer limited by the number of documents in the applicable LLM processor context window.

Due to the limitations of embedding-based similarity search, the document QA task poses a great challenge to all methods. Researchers observed that MemGPT stops paginating crawler results before the crawler database is exhausted.

In addition, there is a trade-off in the retrieval document capacity created by more complex operations of MemGPT, as shown in Figure 7 below. Its average accuracy is lower than GPT-4 (higher than GPT-3.5), but it can be easily extended Larger document.

The researchers also introduced a new task based on synthetic key-value retrieval, namely Nested Key-Value Retrieval, to demonstrate how MemGPT Organize information from multiple data sources.

From the results, although GPT-3.5 and GPT-4 showed good performance on the original key-value task, they performed poorly on the nested key-value retrieval task. MemGPT is not affected by the number of nesting levels and can perform nested lookups by repeatedly accessing key-value pairs stored in main memory through function queries.

MemGPT's performance on nested key-value retrieval tasks demonstrates its ability to perform multiple lookups using a combination of multiple queries.

For more technical details and experimental results, please refer to the original paper.

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