Are Language Models Stuck in the Past?

Large language models (LLMs) have a problem. They tend to freeze in time with outdated knowledge when trained on shuffled datasets. Enter a new approach: training on ordered sequences from Common Crawl snapshots. It seems like a no-brainer, right? But the reality is more complex.

Out with the Old, In with the Timely

This recent study brings forward a key innovation. Researchers introduced a benchmark of over 7,000 questions grounded in time. Why does this matter? It lets us see if these models can associate facts with their correct time periods. Spoiler alert: the results suggest they often can't.

The study tested 6 billion-parameter models. Those trained on temporal sequences showed more up-to-date knowledge than their shuffled counterparts. Translation: if you want your model to know current events, don't shuffle its training data. But don't expect miracles. The funding rate is lying to you again. Improvements were noticeable yet not world-changing.

Repetition, the Enemy of Freshness

There's a catch. Shuffled datasets excel in repeating facts. That might sound good until you realize it peaks on stale data. The sequentially trained models? They did well on freshness but didn't significantly outperform in general language understanding.

So, should we throw out the old shuffled method? Not yet. Everyone has a plan until liquidation hits. Models still need shuffled data for a broader understanding. But there's a case for updating the mix with time-ordered data to keep everything current.

Implications and Future Research

Why should anyone care? If LLMs are to be valuable in real-time applications, they need temporal grounding. No one wants a chatbot quoting stats from 2020 like it's breaking news. This study sets the stage for future research on continual learning for LLMs. The code, checkpoints, and datasets are all up for grabs on GitHub and Hugging Face.

So, what's the takeaway? Zoom out. No, further. See it now? Training methods need evolution if LLMs aim to keep pace with reality. Data order matters more than you'd think.