The Exhausted River of Large Language Models
A river is only worth its value with its flow.
Picture anyone standing beside a river that used to flow vigorously. Now let the memory turn that image into a dry stretch of cracked earth. This image isn’t just a piece of nature; it’s a reflection of a world where new data is slowing down. Large Language Models (LLMs) like GPT-4, Anthropic, and Gemini have changed how we interact with data. They are trained and thrive on data we feed them.
But what if that stream dries up?
It is going to be a big deal. LLMs have turned industries in a new direction, sparked creativity, and helped solve tough problems. They’re powerful because they continuously learn from new data. Take that away, and we’re left wondering how to move forward.
The sad truth is that fresh information is drying up. We have fed LLMS nearly all information accessible to humanity. What interests me is what is on the other side of this?
Knowledge is like water to us. Just as rivers nourish land, new data keeps our tools fertile. Think about what happens to a riverbed when the water stops. It dries out, cracks, and becomes lifeless. The same is going to happen to LLMs.
Or rather, is happening.
But the issue is more nuance than that. New information helps us challenge beliefs and interact with data differently. Without it, we risk reinforcing the same old mistakes. It’s like trying to grow a garden without water. Nothing new can take root, and what’s already there withers. The scenario of data exhaustion is more than just a technological challenge. It’s a profound reflection on our dependence on continuous knowledge.
Maybe it speaks to our addiction to information. As we face the problem of exhausting information, we got to get used to the idea. And we got to get ready for whats on the other side.
The prospect of data exhaustion is troubling. LLMs, when starved of new information, will recycle old data. It will create clones of themselves with slight variations. These “mutations” are already concerning. It’s a scenario where machines mimic life, mutating in small ways to remain relevant. Yet, they never evolve. This stagnation mirrors a disturbing reality for us. We’re heading towards a future trapped in a loop, unable to break free from recycled thoughts and ideas.
As I write this, I know that humanity is constantly creating new reports and materials to enhance our own creativity with LLMs. However, mathematically, we cannot create new information as fast as we have fed LLMs over the last few years.
Does this mean a stagnation of LLM progression?
Or does it mean a mutation of what is currently in play?
Honestly, no one knows. But, let’s talk about it.
The biggest problem we are going to encounter is Data Exhaustion. So, what is this?
Data exhaustion refers to the point where nearly all available data to feed and train LLMs have been used. After all, these models rely on vast datasets to continually improve their performance. Only then can they generate content. The training data encompasses a vast trove of books, articles, websites, and diverse content from the internet. However, data exhaustion is a destination we are heading to. Is it a cliff, or a wall?
As LLM models have ingested a significant portion of the accessible internet, the incremental improvements with new versions have become less pronounced. Researchers have noted that despite increasing complexity of the new models, the quality of their outputs are plateauing. This suggests that simply increasing parameters or computational power is not a viable solution without access to fresh data.
The river of data that once flowed to grow and feed LLMs is drying up.
The consequences of data exhaustion stretch beyond the technical limitations of LLMs. When these large language models start stagnating, it’s going to stifle progress and hinder the development.
Furthermore, data exhaustion highlights a fundamental issue to LLM development. It feels like we are going to have to confront the reality that there is only a finite of information we can feed LLMs. We are approaching the limits of what can train these models.
So, what is the steady state of training LLMs? This situation demands a rethinking of data, collection and sourcing.
Because what is on the other side of this?
The metaphor of a dry riverbed encapsulates this problem. “As the riverbed lies dry, so too does the flow of fresh insights cease, leaving the land of knowledge parched and barren.” Just as a river nourishes the surrounding land, new data sustains and enriches LLMs. When the river dries up, the land becomes desolate. Then life will struggle. Similarly, without new data, the knowledge base becomes barren, and the ecosystem of continuously improving LLMs will dry up.
This stagnation is not just a theoretical concern. It is already observable in the diminishing returns of LLMs. As these models have consumed nearly all of known data, their improvements with each iteration have become less dramatic. The models repeat information, and their outputs become less novel and insightful. This trend is a warning sign that we are getting closer to that destination.
The most alarming part of that is, we do not know what the destination is.
When new data dries up, what then? Human creativity? More pressure on our brains to think in a choatic sense?
Moreover, the scenario of data exhaustion could cause a cultural shift towards valuing intellectual engagement and critical inquiry.
Does data exhaustion mean a re-look at how we create and record data?
The metaphor of a dry riverbed encapsulates this opportunity for renewal and growth. “In the dry riverbed, the seeds of original thought lie dormant, waiting for the rains of inspiration.”
Is it possible for us to become rainmakers to fill the river?
I don’t know.
I hope so.
