Why Your LLMs Keep Getting Facts Wrong

Large Language Models (LLMs) are like the overeager nerds of the AI world. They know a lot, but keeping their facts straight, they can trip over their own smarts. Editing these models to update or correct their knowledge isn't just about sticking new facts in. It's about making sure they think logically too. And that's where things get messy.

The Editing Conundrum

Retraining LLMs every time you need an update? That's like buying a new car because your old one needs an oil change. It's expensive and inefficient. So, we rely on knowledge editing techniques instead. But a glaring issue stands: most methods only make sure the models remember the new facts. What about the ripple effects, the logical conclusions these facts should lead to?

Enter a new benchmark aiming to test precisely this. It doesn't just ask if the model knows the fact, but if it can follow the bread crumbs to related logical truths. Think of it like fact-checking, but for logic.

Mind the Gap

The findings? Well, they're a bit embarrassing for the AI community. Popular methods like ROME and FT can stuff new facts into LLMs, sure. But making the model understand the consequences of these facts, there's a gap. A big one. Up to 24% worse performance when models are tested on their ability to logically infer new knowledge from an edit rather than just recall the fact itself.

Why should you care? Because it means when your AI says it's sure about something, it might be missing the context. It's like a student who memorized the textbook but skipped every discussion class. If you're relying on AI for critical info, that gap isn't just academic. It could be disastrous.

Raising the Bar

So, what needs to change? For starters, the industry needs to prioritize semantics-aware evaluation frameworks in knowledge editing. If nobody would play it without the model, the model won't save it. The same goes for LLMs. If they can't think through what they know, what's the point in updating them?

In the end, this isn't just a challenge for tech wizards in labs. It's a reminder for everyone using AI: Don't take its word as gospel. Dig deeper. Test smarter. Retention curves don't lie, and neither should our models.