The Context Window Paradox with LLMs

Key Takeaways

building an AI agent can sometimes require vast context Windows maybe you're feeding whole documents to your agent or you're working with some super lengthy prompts if you are you're probably looking at anthropic 100K token window and thinking it's the golden ticket but hang on a second a recent study suggests that bigger might not always be better let's chat about [Music] that hey it's Pete and today on cont text we're going to be talking about context Windows now if you've been keeping an eye on large language models you'll have seen that their context windows are getting pretty expansive take anthropic for instance they're boasting a whopping 100K token window then we've got GPT 4 and llama sitting at 32k Google's Palm at 8K and coh here rounding it out at just over 4K but there's this paper that just popped up on archive titled lost in the- Middle how language models use long context that puts into question whether having large context Windows is always the best the researchers took a good hard look at several models and their context windows and here's the kicker across the board there was this kind of u-shaped pattern the models were on point when the info was at the start of the context not so much in the end but in the middle so what's the deal well the paper throws around a few ideas the these include model architecture training bias task design and a few others but the one that really got me thinking was about attention mechanisms okay so you're probably going Pete what the hell is an attention mechanism well here's a little analogy imagine you're in a lecture you don't hang on every single word that the lecturer says instead you focus on key points the bits that seem most important essentially that's what an attention mechanism does in a large language model they help the model decide which parts of the text to zoom in on and prioritize when generating a response and this is where attention mechanisms Behavior becomes particularly intriguing when these models process vast amounts of text their attention seems to be more I guess you could say concentrated at the beginning before kind of thinning out in the middle and then getting this slight uptick at the end essentially the mechanism's efficiency diminishes over longer stretches leading to that u-shape pattern we talked about now this pattern is actually reminiscent of something we humans experience it's called the serial position effect Herman eming house found in 1966 that we tend to remember items at the beginning and the end of a list better than those in the middle it's kind of fascinating isn't it that these llms in some way reflect our own cognitive Tendencies I'm sure it's just a coincidence but hey I thought it was kind of cool so I'd bring it up all right so what's the takeaway well the big thing is you're going to want to put the most important information related to your prompt near the front of the context window and a great way to do that is with retrieval augmented generation or as the cool kids are saying rag here you use a vector database to retrieve information and pass it to the llm as part of the context of the prompt to help make sure that your vector database is doing its job placing the most accurate information up front you'll need to optimize your documents for retrieval and to help here's a quick cheat sheet first up it's all about layout it's all about organizing the content with clear headings and the light to make it easy for the llm to navigate and pinpoint crucial information next summaries start each section with a brief overview letting the model quickly grasp the essence of the section without getting bogged down in the details it's kind of basic but just ensure your documents Spotlight a lot of frequently searched items product names IDs common qu queries and the like so you could have a frequently asked questions section at the start of each document consistent formatting across content helps the model recognize patterns and retrieve info more predictably bullet points help distill complex ideas into digestible chunks making it a breeze for your model to understand and you me llms nobody likes jargon if you've got technical terms throughout your documents make sure you provide explanations it's also good to break up D blocks of text into shorter paragraphs last but not least a table of contents can help models Traverse big documents quickly ultimately if you can make the document human readable then an llm will be able to read it too that was a lot thanks for sticking with me that's my take on context Windows let me know yours in the comments and remember stay [Music] curious [Music]