The human mind has remained inexplicable and mysterious for a long, long time. And looks like scientists have acknowledged a new contender to this list – Artificial Intelligence (AI). At the outset, understanding the mind of an AI sounds rather oxymoronic. However, as AI gradually becomes more sentient and evolves closer to mimicking humans and their emotions, we are witnessing phenomena that are innate to humans and animals – hallucinations.
Yes, it appears that the very trip that the mind ventures into when abandoned in a desert, cast away on an island, or locked up alone in a room devoid of windows and doors is experienced by machines as well. AI hallucination is real and tech experts and enthusiasts have recorded multiple observations and inferences.
In today’s article, we will explore this mysterious yet intriguing aspect of Large Language Models (LLMs) and learn quirky facts about AI hallucination.
What Is AI Hallucination?
In the world of AI, hallucinations don’t vaguely refer to patterns, colors, shapes, or people the mind can lucidly visualize. Instead, hallucination refers to incorrect, inappropriate, or even misleading facts and responses Generative AI tools come up with prompts.
For instance, imagine asking an AI model what a Hubble space telescope is and it starts responding with an answer such as, “IMAX camera is a specialized, high-res motion picture….”
This answer is irrelevant. But more importantly, why did the model generate a response that is tangentially different from the prompt presented? Experts believe hallucinations could stem from multiple factors such as:
- Poor quality of AI training data
- Overconfident AI models
- The complexity of Natural Language Processing (NLP) programs
- Encoding and decoding errors
- Adversarial attacks or hacks of AI models
- Source-reference divergence
- Input bias or input ambiguity and more
AI hallucination is extremely dangerous and its intensity only increases with increased specification of its application.
For instance, a hallucinating GenAI tool can cause reputational loss for an enterprise deploying it. However, when a similar AI model is deployed in a sector like healthcare, it changes the equation between life and death. Visualize this, if an AI model hallucinates and generates a response to the data analysis of a patient’s medical imaging reports, it can inadvertently report a benign tumor as malignant, resulting in a course-deviation of the individual’s diagnosis and treatment.
Understanding AI Hallucinations Examples
AI hallucinations are of different types. Let’s understand some of the most prominent ones.
Factually incorrect response of information
- False positive responses such as flagging of correct grammar in text as incorrect
- False negative responses such as overlooking obvious errors and passing them as genuine
- Invention of non-existent facts
- Incorrect sourcing or tampering of citations
- Overconfidence in responding with incorrect answers. Example: Who sang Here Comes Sun? Metallica.
- Mixing up concepts, names, places, or incidents
- Weird or scary responses such as Alexa’s popular demonic autonomous laugh and more
Preventing AI Hallucinations
AI-generated misinformation of any type can be detected and fixed. That’s the specialty of working with AI. We invented this and we can fix this. Here are some ways we can do this.
Limiting Responses
They say it doesn’t matter how many languages we speak. We need to know when to stop talking in all of them. This applies to AI models and their responses as well. In this context, we can restrict a model’s capability to generate responses to a specific volume and mitigate the chances of it coming up with bizarre outcomes. This is called Regularization and it also involves penalizing AI models for making extreme and stretched outcomes to prompts.
Relevant & Airtight Sources To Cite And Extract Responses
When we are training an AI model, we can also limit the sources a model can refer to and extract information from to just legitimate and credible ones. For instance, healthcare AI models like the one example we discussed earlier can refer only to sources that are credible in information loaded with medical images and imaging technologies. This prevents machines from finding and co-relating patterns from bipolar sources and generating a response.
Defining An AI Model’s Purpose
AI models are quick learners and they just need to be told precisely what they should do. By accurately defining the purpose of models, we can train models to understand their own capabilities and limitations. This will allow them to autonomously validate their responses by aligning generated responses to user prompts and their purpose to deliver clean outcomes.
Human Oversight In AI
Training AI systems are as critical as teaching a kid swimming or cycling for the first time. It requires adult supervision, moderation, intervention, and hand-holding. Most AI hallucinations occur due to human negligence in different stages of AI development. By deploying the right experts and ensuring a human-in-the-loop workflow to validate and scrutinize AI responses, quality outcomes can be achieved. Besides, models can be further refined for accuracy and precision.
Shaip And Our Role In Preventing AI Hallucinations
One of the other biggest sources of hallucinations is poor AI training data. What you feed is what you get. That’s why Shaip takes proactive steps to ensure the delivery of the highest quality data for your generative AI training needs.
Our stringent quality assurance protocols and ethically sourced datasets are ideal for your AI visions in delivering clean outcomes. While technical glitches can be resolved, it is vital that concerns about training data quality are addressed at their grassroots levels to prevent reworking on model development from scratch. This is why your AI and LLM training phase should start with datasets from Shaip.
