Large language models (LLMs) are impressive, but they sometimes 'hallucinate,' making up facts that aren't real. A new research paper, 'CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control,' tackles this problem with a clever approach: adaptive retrieval. Imagine an LLM writing an essay. Instead of constantly looking up information, it only checks external sources when it's unsure about something. This 'adaptive retrieval' prevents information overload and keeps the writing focused. CtrlA goes a step further by focusing on the LLM's 'honesty' and 'confidence.' It figures out when the LLM is bluffing or genuinely doesn't know. This helps decide when to trigger a search for external information. The researchers also improved how the LLM phrases its search queries, making the retrieval process more efficient. Tests on various question-answering datasets showed CtrlA outperforms existing methods, generating more accurate and truthful responses. This research is a significant step towards making LLMs more reliable and trustworthy. By understanding their limitations and knowing when to seek external validation, LLMs can become even more powerful tools for communication, research, and creative writing. However, challenges remain, such as fine-tuning the balance between internal knowledge and external retrieval. Future research could explore more sophisticated methods for extracting honesty and confidence features, further refining the adaptive retrieval process and paving the way for even more truthful and reliable LLMs.