Large Language Models (LLMs) are impressive, but they don't know everything. Sometimes, they confidently give wrong answers because they lack the necessary information. Researchers are exploring how to make LLMs smarter by teaching them when to search for information instead of relying solely on their internal knowledge. This new approach, called "adaptive retrieval," trains LLMs to recognize when they need more context. Imagine an LLM encountering a question it can't answer confidently. Instead of guessing, it generates a special signal, essentially saying, "I need to look this up!" This signal triggers a search for relevant information, which is then fed back to the LLM for a more accurate answer. This is like giving an LLM the ability to "Google" when it's stumped. Experiments show that this adaptive approach outperforms methods where LLMs either always or never search for external information. It's particularly effective for questions about less common topics, where the LLM's internal knowledge might be insufficient. One key finding is that the quality of the search engine significantly impacts the LLM's performance. If the search engine doesn't find the right information, the LLM still struggles. This highlights the need for better search technologies to complement these adaptive LLMs. The future of this research involves improving the search process and exploring how different training datasets affect the LLM's ability to learn when to retrieve information. This "adaptive retrieval" approach is a promising step towards more reliable and knowledgeable LLMs.