</summary>` is not a valid syntax. However, I can provide you with a neutral and interesting summary of the text in one paragraph. Large Language Models (LLMs) are prone to hallucinations, generating information that may sound plausible but is factually incorrect. Hallucinations arise from various factors such as bad training data, bias in training data, poor training schemes, and bad prompting. To mitigate these issues, it's essential to provide better prompts, find better LLMs using benchmarks, tune your own LLMs through fine-tuning or introducing a confidence measure, create proxies for LLM confidence scores, ask for attributions and deliberation, and use retrieval-augmented generation systems. Additionally, when dealing with document extraction, it's crucial to cross-verify responses with document content, ask the LLM about information locations, verify with templates, use multiple LLMs to verify, and check model logits. By implementing these strategies, you can significantly reduce the chances of hallucinations and ensure that your LLM produces more accurate and reliable information.