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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 views • 11 months ago •via X (Twitter)

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Chroma's profile picture
Chroma11 months ago

Read the full report here:

Mobile Scanner's profile picture
Mobile Scanner1 year ago

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

jason liu's profile picture
jason liu11 months ago

wow veo3 is so good

Kinjal Nandy's profile picture
Kinjal Nandy11 months ago

Lfg @kellyhongsn

noah's profile picture
noah11 months ago

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

dinos's profile picture
dinos11 months ago

you guys are on fire

Chroma's profile picture
Chroma11 months ago

spread the news

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Allan Ryan11 months ago

Is she AI?

Chroma's profile picture
Chroma11 months ago

100% human intelligence

sarv's profile picture
sarv11 months ago

Wooo @kellyhongsn!!

Olivier's profile picture
Olivier11 months ago

benchmark different context engineering strategies next good content

Related Videos

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 views • 1 year ago