Large language models (LLMs) like ChatGPT seem to know a vast amount of information. But do they truly *retain* everything they learn? New research explores the surprising phenomenon of 'forgetting' during LLM pre-training – the initial phase where the model absorbs massive amounts of text data. This isn't about privacy leaks or remembering specific user prompts. It's about how LLMs handle factual knowledge, especially information tied to real-world entities. Traditional metrics like perplexity don't fully capture this kind of forgetting. The study introduces new, entity-focused metrics that reveal a more nuanced picture of memory retention in LLMs. The findings show that as LLMs learn more, they can actually become *worse* at recalling information about entities they've previously encountered. This raises important questions about how we train these powerful models. Researchers experimented with 'memory replay' techniques, similar to how humans review material to reinforce learning. They found that by strategically revisiting previously learned information, LLMs can better hold onto factual knowledge. Intriguingly, the research also suggests that, just like humans, LLMs benefit from more intensive learning sessions when trying to remember challenging information. The study of 'forgetting curves' in LLMs reveals parallels to human memory, hinting that periodic, intensive review could be crucial for preventing knowledge loss in AI. This research is a significant first step towards understanding the complex dynamics of memory in LLMs. It highlights the importance of developing training methods that prioritize long-term knowledge retention, paving the way for more reliable and knowledgeable AI systems in the future.