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Introducing our latest technical report: Context Rot - How Increasing Input Tokens Impacts LLM Performance Our results reveal that models do not use their context uniformly. full report in replies

184,747 Aufrufe • vor 11 Monaten •via X (Twitter)

11 Kommentare

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Chromavor 11 Monaten

Read the full report here:

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Mobile Scannervor 1 Jahr

Scan any documents, convert images into text, PDF files, etc. 👍

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jason liuvor 11 Monaten

wow veo3 is so good

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Kinjal Nandyvor 11 Monaten

Lfg @kellyhongsn

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noahvor 11 Monaten

feel like we've all known this so im glad it was rigerously tested

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dinosvor 11 Monaten

you guys are on fire

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Chromavor 11 Monaten

spread the news

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Allan Ryanvor 11 Monaten

Is she AI?

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Chromavor 11 Monaten

100% human intelligence

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sarvvor 11 Monaten

Wooo @kellyhongsn!!

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Oliviervor 11 Monaten

benchmark different context engineering strategies next good content

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New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,729 Aufrufe • vor 1 Jahr