Here’s another little experiment I built over the break. Like pretty much everyone these days, I am fascinated by the potential of applying large language models (LLMs) to generative use cases. So I wanted to see what’s what. As my target, I picked a fairly straightforward use case: AI as the narrator.

While I share in the excitement around applying AIs as chatbots and pure generators of content, I also understand that predictive models, no matter how sophisticated, will have a hard time crossing the chasm of the uncanny valley – especially when pushed to its limits. As a fellow holder of a predictive mental model, I am rooting for them, but I also know how long this road is going to be. We’ll be in this hollow of facts subtly mixed with cheerful hallucination for a while.

Instead, I wanted to try limiting the scope of where LLM predictions can wander. Think of it as putting bounds around some text that I (the user) deem as a potential source of insights and asking the AI to only play within these bounds. Kind of like “hey, I know you’ve read a lot and can talk about nearly anything, but right now, I’d like for you to focus: use only these passages from War and Peace for answering my questions”.

In this setup, the job of an LLM would be to act as a sort of well… a language model! This is a terrible analogy, but until I have a better one, think of it being a speech center of a human brain: it possesses incredible capabilities, but also, when detached from the rest of the prefrontal cortex, produces conditions where we humans just babble words. The passages from War and Peace (or any other text) act as a prefrontal cortex, grounding the speech center by attaching it to the specific context in which it offers a narration.

Lucky for me, this is a well-known pattern in current LLM applications. So, armed with a Jupyter cookbook, I set off on my adventure.

A key challenge that this cookbook overcomes is the limited context window that modern LLM applications have. For example, GPT-3 is only capable of just over 4000 tokens (or pieces of words), which means that I can’t simply stuff all of my writings into context. I have to be selective. I have to pick the right chunks out of the larger corpus to create the grounding corpus for the narrator. So, for example, if I have questions about Field Marshal Kutuzov eating chicken at Borodino, yet I give my narrator only the passages from Natasha’s first ball, I will get unsatisfying results.

I won’t go into technical details of how that’s done in this post (see the cookbook for the general approach), but suffice to say, I was very impressed with the outcomes. After I had the code running (which, by the way, was right at the deca-LOC boundary), my queries yielded reasonable results with minimal hallucinations. When in the stance of a narrator, LLM’s hallucinations look somewhat different: instead of presenting wrong facts, the narrator sometimes elides important details or overemphasizes irrelevant bits.

Now that I had a working “ask ‘What Dimitri Learned’ a question” code going, I wondered if the experience could be more proactive. What if I don’t have a question? What if I didn’t know what to ask? What if I didn’t want to type, and just wanted to click around? And so the Wanderer was born.

Here’s how it works. When you first visit wanderer.glazkov.com, it will pull a few random chunks of content from the whole corpus of my writings, and ask the LLM to list some interesting key concepts from these chunks. Here, I am asking the narrator not to summarize or answer a specific question, but rather discern and list out the concepts that were mentioned in the context.

Then, I turn this list of concepts into a list of links. Intuitively, if the narrator picked out a concept from my writings, there must be some more text in my writings that elaborates on this concept. So if you click this link, you are asking the wanderer to collect all related chunks of text and then have the narrator describe the concept.

Once you’ve read the description, provided by Wanderer, you may want to explore related concepts. Again, I rely on the narrator to list some interesting key concepts related to the concept. And so it goes.

When browsing this site (or “wandering”, obvs), it may feel like there’s a structure, a taxonomy of sorts that is maintained somewhere in a database. One might even perceive it as an elaborate graph structure of interlinked concepts. None of that exists. The graph is entirely ephemeral, conjured out of thin air by the narrating LLM, disappearing as soon as we leave the page. In fact, even refreshing the page may give you a different take: a different description of the concept or a different list of related concepts. And yes, it may lead you into dead ends: a concept that the narrator discerned, yet is failing to describe given the context.

The overall experience is surreal and – at least for me – highly generative. After wandering around for a while, it almost feels like I am interacting with a muse that is remixing my writing in odd, unpredictable, and wonderful ways. I find that I use the Wanderer for inspiration quite a bit. What concepts will it dig up next? What weird folds of logic will be unearthed with the next click? And every “hmm, that’s interesting” is a spark for new ideas, new connections that I haven’t considered before.

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