Understanding Hallucination Rates in Language Models: Insights from Training on Knowledge Graphs and Their Detectability Challenges

" data-medium-file="https://www.marktechpost.com/wp-content/uploads/2024/08/Screenshot-2024-08-18-at-1.07.31-AM-300x198.png" data-large-file="https://www.marktechpost.com/wp-content/uploads/2024/08/Screenshot-2024-08-18-at-1.07.31-AM-1024x676.png" tabindex="0" role="button"> " data-medium-file="https://www.marktechpost.com/wp-content/uploads/2024/08/Screenshot-2024-08-18-at-1.07.31-AM-300x198.png" data-large-file="https://www.marktechpost.com/wp-content/uploads/2024/08/Screenshot-2024-08-18-at-1.07.31-AM-1024x676.png" tabindex="0" role="button">Language models (LMs) exhibit improved performance with increased size and training data, yet the relationship between model scale and hallucinations remains unexplored. Defining hallucinations in LMs presents challenges due to their varied manifestations. A new study from Google Deepmind focuses on hallucinations where correct answers appear verbatim in training data. Achieving low hallucination rates demands […]

The post .