Causes of Artificial Intelligence (AI) Hallucinations
Some of the reasons (or causes) why Artificial Intelligence (AI) models do so are:
- Quality dataset: AI models rely on the training data. Incorrect labelled training data (adversarial examples), noise, bias, or errors will result in model-generating hallucinations.
- Outdated Data: The world is constantly changing. AI models trained on outdated data might miss crucial information or trends, leading to hallucinations when encountering new situations.
- Missing context in training (or test) data: wrong or contradictory input may result in hallucinations. This is in users control to provide the right context in the input.
More often, we rely on the results generated by an AI model, considering they might be accurate ones. But AI models can generate convincing information which can be false. This happens mostly with LLMs trained on data with the above-defined issues. But how can we detect Hallucinations?
Artificial Intelligence Hallucinations
The term āhallucinationā takes on a new and exciting meaning in artificial intelligence (AI). Unlike its meaning in human psychology, where it relates to misleading sensory sensations, AI hallucination refers to AI systems generating imaginative novel, or unexpected. These outputs frequently exceed the scope of training data.
In this post, we will look into the concept of AI hallucination problems, causes, detections, and prevention in the field of AI.
Table of Content
- What is Artificial Intelligence Hallucinations or AI Hallunication?
- Real-World Example of an Artificial Intelligence (AI) Hallucination
- Causes of Artificial Intelligence (AI) Hallucinations
- How Can Hallucination in Artificial Intelligence (AI) Impact Us?
- How can we Detect AI Hallucinations?
- How to Prevent Artificial Intelligence (AI) Hallucinations?
- Conclusion