Search Results: LLM_Hallucinations

Google Research introduces SLED, a novel decoding technique that improves LLM factuality by leveraging outputs from all transformer layers. The method reduces hallucinations by 16% on benchmarks without external data or fine-tuning, offering a lightweight solution to AI's accuracy crisis.

IBM's latest deep dive reveals how Retrieval-Augmented Generation (RAG) tackles LLMs' hallucination problem by grounding responses in real-world data. This hybrid architecture promises more accurate, verifiable AI outputs while sidestepping costly retraining—potentially transforming enterprise AI adoption.