Hallucination
When AI models generate responses, sometimes the outputs stray from the original instructions or context, creating fabricated or irrelevant content. This phenomenon is known as hallucination.
Hallucinations can be frustrating, especially when the AI’s reliability matters. Whether you’re building a chatbot, summarising documents, or generating answers, identifying hallucinations is key to improving accuracy and trust in your AI system.
Hallucinations happen when the output:
- Doesn’t follow the user’s instructions.
- Introduces information that isn’t part of the given context.
- Strays into unrelated topics or makes unsupported claims.
Types of Hallucinations
- Factual Hallucinations: The model introduces information that is factually incorrect or unsupported.
- Contextual Hallucinations: The model generates content unrelated to the given context or query.
- Instructional Hallucinations: The model deviates from the user’s explicit instructions or task.
Identifying Hallucination
Identifying the hallucinations is essential because it ensures accuracy by minimising errors in AI-generated content. This is especially critical in applications such as customer service, research, and education, where reliable information is paramount. By identifying and addressing hallucinations, AI systems can deliver consistent, grounded responses, fostering user trust and instilling confidence in the system’s capabilities.
Click here to learn how to identify hallucination in AI responses