Hallucination in Generative AI ?

What Hallucination is ?

Hallucination in generative AI refers to the phenomenon where an AI model generates information that is not based on the input data or real-world knowledge. This can results in outputs that are factually incorrect , misleading, or entirely fabricated.

Hallucination can occur in various types of generative models , including language models , image generation models and more.

Types:

• Factual Hallucination : The model generates incorrect or non-existent facts.
• Contextual Hallucination : The model produces outputs that do not align with the context or input data.

What are the causes of Hallucination?

• Training Data : Inadequate or biased training data can lead to hallucination.
• Model Architecture: Certain model architectures may be more prone to generating hallucination.
• Inference process: The way the model processes and generate outputs can contribute to hallucination.

How to Mitigate Hallucination? (Mitigating strategies) :

1. Improving Training Data : Ensuring that the training data is comprehensive , accurate and representative of real-world scenarios .
2. Model Fine-Tuning : Fine-tuning the model on specific datasets to reduce the likelihood of hallucination.
3. Post-Processing: Implementing post-processing steps to verify and correct the generated outputs .
4. Human -in-the-Loop: Incorporating human oversight to review and validate the outputs generated by the models.