Hallucination in large language models (LLMs) refers to generating unfaithful, fabricated, or nonsensical content not grounded in provided context or world knowledge. The focus is on extrinsic hallucination, emphasizing the need for LLMs to produce factual content and acknowledge when they lack knowledge. Causes of hallucination include issues during pre-training and fine-tuning stages. Various methods, like retrieval-augmented generation (RAG), special sampling methods, and fine-tuning for factuality, are explored to minimize hallucinations. Evaluation benchmarks and different detection approaches are also discussed.
Table of contents
What Causes Hallucinations? #Hallucination Detection #Anti-Hallucination Methods #Appendix: Evaluation Benchmarks #Citation #References #133 Impressions