Computation and Language - Large Language Models as Interpolated and Extrapolated Event Predictors

Alright Learning Crew, Ernis here, ready to dive into some fascinating research! Today, we're unpacking a paper that looks at how we can use the power of Large Language Models, think super-smart AI text generators, to predict future events based on what's happening in the world right now.

Imagine you’re trying to understand a complex news story. You might break it down into simple pieces: who did what to whom, and when it happened. Researchers are doing something similar, but on a much larger scale.

They're taking real-world events and turning them into these little packages called "quadruples." Each quadruple contains four pieces of information: the subject (who's doing something), the relation (what they're doing), the object (who or what they're doing it to), and a timestamp (when it happened). Think of it like a little news headline condensed into data. For example: "Elon Musk (subject) bought (relation) Twitter (object) in 2022 (timestamp)."

Sometimes, they even add a fifth piece – a short text summary describing the event – making it a "quintuple." This gives the AI even more context.

Now, traditionally, researchers have used things like graph neural networks (GNNs) and recurrent neural networks (RNNs) – basically, complex computer programs – to look at these quadruples and quintuples and try to predict what might happen next. These are like intricate webs that map out relationship and patterns over time.

But this paper asks: what if we could use those big, powerful Large Language Models (LLMs) instead? The kind that can write essays and answer complex questions? Can they do just as well, or even better, at predicting future events?

That's where LEAP comes in. This paper proposes a new framework, called LEAP, that uses LLMs to predict events. Think of LEAP as a system that asks the LLM questions based on the event data.

For example, if we know "Elon Musk bought Twitter in 2022," LEAP might ask the LLM: "Given that Elon Musk bought Twitter in 2022, what might happen next related to Elon Musk and Twitter?"

"LEAP leverages large language models as event predictors."

The researchers designed clever "prompt templates" to help the LLM understand the questions and give the best possible answers. It's like training the LLM to be a super-powered event forecaster!

What's really cool is that, for predicting multiple events in the future, LEAP uses a simplified approach. Instead of those complex GNNs and RNNs, it uses the LLM to create a sort of "snapshot" of each event, then uses a simpler system to analyze those snapshots and predict future relationships. This makes the whole process more efficient.

So, why does this matter?

• For Businesses: Imagine predicting supply chain disruptions or shifts in consumer behavior.
• For Policymakers: Think about forecasting potential social unrest or economic downturns.
• For Everyday Life: Perhaps even anticipating trends in technology or the stock market.

The researchers tested LEAP on real-world datasets and found that it works really well! In some cases, it performed just as well as, or even better than, the traditional methods, while being simpler to implement.

This research suggests that LLMs could revolutionize how we predict future events, making it easier and more accessible for everyone.

Here are a couple of things I'm wondering:

• Given that LLMs are trained on existing data, could this approach inadvertently perpetuate existing biases when predicting future events?
• How adaptable is LEAP to completely novel events or situations that haven't been well-documented in the past?

That's all for this episode, Learning Crew! Let me know what you think about using LLMs for event prediction. Until next time, keep learning!

Credit to Paper authors: Libo Zhang, Yue Ning