Multiple hypothesis testing presents a fundamental challenge in statistical inference: as the number of simultaneous tests increases, so does the probability of false discoveries. This survey provides a comprehensive overview of False Discovery Rate (FDR) control methods, from the seminal Benjamini-Hochberg procedure to modern adaptive and structure-aware algorithms.
Hallucination in large language models (LLMs) remains a critical barrier to reliable deployment in high-stakes applications. This survey systematically analyzes 15 peer-reviewed papers on hallucination detection and mitigation, organizing techniques into a comprehensive taxonomy.
Large Language Models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities in generation, reasoning, and knowledge-intensive tasks. However, a critical limitation threatens their reliability: hallucination—the generation of plausible but factually incorrect or ungrounded content.