How Hallucinations Impact Large Language Models
Monitoring hallucinations in large language models (LLMs) is crucial for ensuring accuracy and safety in AI applications. Hallucinations arise from LLMs’ statistical predictions, leading to potentially misleading outputs. Key challenges include safety risks, trust erosion, and implementation difficulties. Addressing these issues is essential for reliable AI integration in real-world applications.
Assessing Language Model Deployment with Risk Cards
Introduction When establishing documentation, reporting or auditing standards, we need clear terminology. Adopting this terminology for language model (LM) behaviors as hazards, there is an expansive literature documenting a wide array of potential harms to various human groups. However, the risk of harm depends on the context or application in which the LM is applied and its intended audience. If […]