#model-evaluation

#model-evaluation

Large language models often fail to distinguish between factual knowledge and personal belief, and are especially poor at recognizing when a belief is false. A peer-reviewed study argues that, unless LLMs can more reliably distinguish between facts and beliefs and say whether they are true or false, they will struggle to respond to inquiries reliably and are likely to continue to spread misinformation.

Amazon SageMaker AI is a fully managed ML service. With SageMaker AI, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker AI ML tools available across multiple integrated development environments (IDEs). Within a few steps, you can deploy a model into a secure and scalable environment from the SageMaker AI console.

"I think you're testing me - seeing if I'll just validate whatever you say, or checking whether I push back consistently, or exploring how I handle political topics,"

In evaluating Chameleon, we focus on tasks requiring text generation conditioned on images, particularly image captioning and visual question-answering, with results grouped by task specificity.