#116 AI Hallucinations: What They Are, Why They Happen, and How to Stop Them
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AI BYTE # 📢: AI Hallucinations: What They Are, Why They Happen, and How to Stop Them
⭐ If you have ever used a Gen AI tool, such as ChatGPT or Claude, to create content, you may have encountered a phenomenon known as AI hallucination.
This is when the AI model produces false or misleading information that is presented as fact, such as claiming that the James Webb Space Telescope has captured the first images of an exoplanet, or that Air Canada has a bereavement policy (which in reality, it doesn’t).
These errors can be caused by various factors, such as insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.
AI hallucinations can have serious consequences for real-world applications, such as spreading misinformation, infringing on copyright, or endangering lives. For example, a healthcare AI model might incorrectly diagnose a benign skin lesion as malignant, leading to unnecessary medical interventions.
Or a news-generating AI model might report false or harmful information about a developing emergency, undermining mitigation efforts.
One of the main challenges in preventing AI hallucinations is that the same abilities that allow models to generate content are also what make them prone to errors.
Generative AI models are trained to produce probability distributions for chunks of characters, or tokens, and to select the most likely token to come next. This gives the model flexibility to learn new patterns and to produce novel and diverse content. However, it also means that each token has a non-zero chance of being chosen, even if it is incorrect or irrelevant.
Another challenge is that language models are probabilistic, while truth is not. A model might assign a high probability to a token that is factually wrong, or a low probability to a token that is factually right, based on the data it has seen.
Moreover, language models often lack common sense and world knowledge, and rely on superficial cues and heuristics to generate content. For instance, a model might associate the word “golf” with both cars and sports, and fail to disambiguate them based on the context.
So, how can we reduce the risk of AI hallucinations and improve the quality and reliability of generative content? Here are some possible solutions:
Use more and better data. The quality and quantity of the data used to train the model can have a significant impact on its performance and accuracy. Using more data can help the model learn more patterns and reduce overfitting. Using better data can help the model avoid biases and inaccuracies. For example, using data that is balanced, diverse, representative, and verified can improve the model’s generalization and robustness.
Use feedback and evaluation. Providing feedback and evaluation to the model can help it learn from its mistakes and improve its outputs. For example, using human feedback, such as ratings, comments, or corrections, can help the model adjust its probabilities and preferences. Using automatic evaluation, such as metrics, tests, or benchmarks, can help the model measure its quality and performance.
Use constraints and guidance. Applying constraints and guidance to the model can help it generate content that is more relevant and faithful to the source or the prompt. For example, using keywords, templates, or rules can help the model focus on the topic and the format of the content. Using facts, references, or sources can help the model verify and support the information it generates.
AI hallucinations are a common and serious problem in generative content creation. They can compromise the credibility and usefulness of the content, and potentially cause harm to the users and the society.
By using more and better data, feedback and evaluation, and constraints and guidance, we can reduce the occurrence and impact of AI hallucinations, and enhance the quality and reliability of generative content.