Hallucinations in Generative AI: A Brief Overview

Generative AI is a branch of artificial intelligence that focuses on creating new content, such as images, text, music, or speech, from data. Generative AI models learn from existing examples and try to mimic their style, structure, and content. Some of the most popular generative AI techniques are generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers.

However, generative AI models are not perfect. Sometimes, they produce outputs that are unrealistic, distorted, or nonsensical. These outputs are often called hallucinations, because they resemble the perceptual distortions that occur in some mental disorders or drug-induced states. Hallucinations in generative AI can have various causes, such as insufficient data, overfitting, mode collapse, or adversarial attacks.

In this blog post, we will explore some of the common types of hallucinations in generative AI, their possible explanations, and their implications for the future of this field.

Image Hallucinations

Image hallucinations are one of the most visible and striking examples of generative AI failures. Image hallucinations occur when a generative model produces an image that does not correspond to reality or the intended concept. For instance, a model that is supposed to generate faces might produce images with missing or extra features, such as eyes, noses, mouths, or ears. Or a model that is supposed to generate landscapes might produce images with unnatural colors, shapes, or textures.

Image hallucinations can be caused by several factors. One of them is insufficient data. If a model is trained on a limited or biased dataset, it might not learn the full diversity and variability of the real world. For example, if a model is trained on images of faces that are mostly white and young, it might not be able to generate realistic faces of other races and ages. Another factor is overfitting. If a model is trained too long or too closely on a specific dataset, it might memorize some of the examples and reproduce them exactly or with minor variations. For example, if a model is trained on images of cats that have a certain pattern or color, it might not be able to generate cats that look different from the training data.

Another factor that can cause image hallucinations is mode collapse. Mode collapse occurs when a generative model learns to produce only a few modes or categories of outputs, ignoring the rest of the data distribution. For example, if a model is supposed to generate images of animals, it might learn to generate only images of dogs or birds, and ignore other animals like cats or fish. Mode collapse can happen when the model’s objective function is too simple or too harsh, or when the model’s architecture is too constrained or too complex.

A final factor that can cause image hallucinations is adversarial attacks. Adversarial attacks are deliberate attempts to fool or manipulate a generative model by feeding it malicious inputs or modifying its outputs. For example, an attacker might add noise or perturbations to an image to make it look like something else to the model. Or an attacker might alter the output of a model to make it look more realistic or more appealing to human observers. Adversarial attacks can have various motives, such as sabotage, deception, fraud, or entertainment.

What are text hallucinations?

Text hallucinations are outputs generated by a generative AI model that do not match the input data or the intended task. For example, a text hallucination can be a sentence that is grammatically incorrect, semantically inconsistent, factually wrong, or ethically problematic.

Text hallucinations can occur for various reasons, such as:

• Data quality: The generative AI model may have been trained on noisy, biased, or incomplete data, which can affect its ability to learn the correct patterns and rules of the target domain.
• Model architecture: The generative AI model may have a design flaw or a limitation that prevents it from capturing the complexity and diversity of the input data or the output space.
• Model training: The generative AI model may have been trained with inappropriate hyperparameters, objectives, or evaluation metrics, which can lead to overfitting, underfitting, or mode collapse.
• Model inference: The generative AI model may have been used with improper sampling methods, decoding strategies, or post-processing techniques, which can introduce errors or artifacts in the output.

Why are text hallucinations problematic?

Text hallucinations can have negative impacts on the quality and usability of generative AI systems. For example:

• Text hallucinations can reduce the credibility and trustworthiness of generative AI systems, especially if they produce outputs that are misleading, inaccurate, or offensive.
• Text hallucinations can affect the performance and efficiency of generative AI systems, especially if they produce outputs that are irrelevant, redundant, or contradictory.
• Text hallucinations can expose the vulnerabilities and biases of generative AI systems, especially if they produce outputs that are discriminatory, harmful, or illegal.

How can text hallucinations be detected and prevented?

Text hallucinations are hard to detect and prevent because they depend on the context and the purpose of the generative AI system. However, some possible solutions are:

• Data cleaning: The input data should be carefully curated and filtered to remove noise, outliers, duplicates, or anomalies that can confuse the generative AI model.
• Data augmentation: The input data should be enriched and diversified to cover more scenarios, variations, and perspectives that can enhance the generality and robustness of the generative AI model.
• Data labeling: The input data should be annotated and categorized to provide more information, guidance, and feedback to the generative AI model.
• Model regularization: The generative AI model should be constrained and penalized to avoid overfitting or underfitting the input data or the output space.
• Model evaluation: The generative AI model should be tested and validated on different datasets, tasks, and metrics to measure its quality and diversity.
• Model monitoring: The generative AI model should be supervised and controlled to detect and correct any errors or anomalies in the output.