Large language models (LLMs) like ChatGPT have become incredibly sophisticated, capable of writing stories, translating languages, and even generating code. But what if these impressive feats are partly an illusion, a result of the model simply regurgitating information it's already seen? A new research survey dives deep into the problem of "data contamination," where the data used to test an LLM overlaps with the massive datasets it was trained on. This can lead to artificially inflated scores on benchmarks, making a model appear far more capable than it actually is. Imagine studying for a test using the answer key – you'd ace the exam, but you wouldn't have truly learned the material! Similarly, an LLM exposed to test data during training might score high, but its ability to generalize to new, unseen problems is compromised. The survey explores various ways contamination happens. Since LLMs are often trained on massive datasets scraped from the internet, there's a good chance they've already seen parts of benchmark datasets. The survey also categorizes different detection methods, from simple string matching to more advanced techniques that probe a model's behavior to reveal memorization. But detection is only half the battle. The survey discusses strategies for mitigating contamination, such as creating dynamic benchmarks that constantly evolve or even encrypting test data to prevent leakage. Perhaps the biggest takeaway is a need to rethink how we evaluate LLMs. As these models become more powerful and their training datasets grow larger, standard evaluation metrics might no longer be sufficient. The challenge becomes less about distinguishing between training and testing data and more about assessing a model’s true understanding and ability to handle real-world problems. Data contamination raises crucial questions about the trustworthiness and transparency of LLM evaluations. This research provides a valuable roadmap for navigating this complex issue and building more robust and reliable AI systems.