Understanding AI Hallucination: A New Perspective
In the world of Artificial Intelligence, the term “hallucination” is often used to describe a range of errors. However, not all AI misses fall under this label. It’s crucial to differentiate types of failures to seek appropriate solutions. Here’s a breakdown:
- Omitted Scope: When a model performs well but misses unmentioned requirements, it’s not a hallucination; it’s a scope issue.
- Default Fill-In: Sometimes, the model fills gaps with plausible information. This needs clarity in input rather than correction for creativity.
- Blended Inference: AI can mix grounded facts with assumptions, producing ambiguous responses that require careful evaluation.
💡 Why This Matters: Correctly diagnosing these failures paves the way for targeted fixes. With the Verified/Deduction/Gap (VDG) model, we aim to clarify these distinctions and enhance AI outputs.
🔗 Let’s redefine how we discuss AI performance! Share your thoughts below and let’s foster insightful conversations!
