Scalability of CIBER's Evidence Retrieval and Its Impact on Scientific Claim Verification Accuracy with Up to 1000 Documents

Large Language Models(LLMs) now handle tasks like question answering, summarisation, code generation and dialogue with impressive results.Yet they still suffer from a key issue: hallucination happens when a model generates text that reads well but is factually wrong or not grounded in real evidence.The risk is higher in domains like healthcare, law, finance and research, where inaccurate outputs can lead to real damage.This survey focuses on how to detect hallucination verification in LLMs.We review 5 core detection approaches: retrieval-based, uncertainty-based, embedding-based, learning-base

Research goal: How does the scalability of CIBER's evidence retrieval mechanism affect the accuracy of scientific claim verification when integrating up to 1000 corroborating/refuting documents per claim?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.9/10.