The Real Reason AI Models Hallucinate
GitHub presents an overview of why AI models hallucinate, summarizing OpenAI’s research on the issue and highlighting potential strategies to mitigate confidently wrong responses.
The Real Reason AI Models Hallucinate
Ever gotten a confidently wrong answer from a chatbot? That’s known as an AI hallucination. In this video, GitHub explains the phenomenon using findings from a recent OpenAI paper.
What Is an AI Hallucination?
AI hallucination occurs when large language models (LLMs) generate answers that sound convincing but are factually incorrect. This isn’t just a simple bug—it’s often a byproduct of the way these models are trained.
Why Does Hallucination Happen?
LLMs are trained on vast datasets with the goal of predicting the most likely next word or sentence. During this process, models sometimes generalize or ‘fill gaps’ in ways that lead to incorrect outputs if the training data doesn’t match the specific user question. The OpenAI paper suggests hallucinations are a systemic side effect, not an accidental flaw.
Potential Solution: Rewarding Humility
The paper argues that a promising approach to reducing hallucinations is to encourage models to admit when they don’t know an answer, rather than over-confidently guessing. Training AI to express uncertainty more often could improve trust and reliability.
Learn More
- OpenAI Research Paper
- Stay up-to-date on GitHub’s channels:
About GitHub: GitHub is where over 100 million developers collaborate, build, and share code. Learn more.