How can scientists reliably detect and mitigate hallucinations in large language models used for scientific writing and literature review?

Large language models are increasingly being utilized for literature reviews, manuscript drafting, and scientific question answering. Hallucinated citations, unsupported assertions and factual errors remain major problems, however. What evaluation methods, benchmarks, human-in-the-loop strategies, or retrieval-augmented strategies have experts found to be most effective in improving factual reliability while maintaining efficiency in scientific research workflows?