Turing Test
Ethics & Safety
The extent to which an LLM's outputs accurately reflect facts and avoid hallucinations.
Detailed Explanation
Truthfulness in large language models (LLMs) refers to the model's ability to generate outputs that accurately represent factual information, minimizing hallucinations—erroneous or fabricated details. Ensuring truthfulness is crucial for reliable, trustworthy AI systems, as it helps prevent misinformation and supports ethical use by providing users with accurate, verified knowledge aligned with real-world facts.
Use Cases
- •Use case: Enhancing a medical chatbot to ensure accurate diagnosis information and minimize hallucinated, incorrect medical advice.
Related Terms
Other terms in the Ethics & Safety category