AI Hallucination: Sorting Facts from Fiction

AI Hallucination
Table of Contents

AI-driven chatbots occasionally generate content that isn’t based on actual facts, presenting it confidently as if it were accurate. This subtle misinformation has presented numerous challenges for technology companies and has often been highlighted in media reports.

These inaccuracies, known as AI hallucinations, can be seen as a flaw and a natural part of how these systems operate. It’s crucial to differentiate between using these tools for generating creative content and depending on them for accurate information.

In this article, we will clarify what AI hallucinations are and why they occur.

What is an AI Hallucination?

AI hallucination is the phenomenon by which artificial intelligence systems, especially large language models (LLMs), provide spurious, misleading, or nonsensical output. This output may arise from defects in the training data or inadequacies in the model’s ability to determine the reliability of its source.

Some examples of hallucinations in AI include confidently stated falsehoods, illogical results, and outright invented narratives.

The problem isn’t a major hazard for small talk with chatbots and digital assistants. However, it can lead to serious mistakes in contexts where accuracy and getting the facts straight are important, such as law, healthcare, and finance.

That is why experts advocate for integrating generative AI tools with robust fact-checking mechanisms, ensuring no chatbot operates without supervision. 

This approach will help mitigate risks and improve the reliability of AI-generated content. Also, it will tackle the challenge of AI hallucinations, which arise as a byproduct of models performing as they were trained – to mimic training data without a requisite for factual accuracy.

Now, let’s look into why and how AI hallucinates in more detail.

Why and How Does AI Hallucinate?

LLMs that drive generative AI tools are built on extensive datasets consisting of articles, books, code, and social media posts. These models are quite adept at crafting text that closely resembles what they’ve been trained on.

Take, for example, a model that has never come across the word “azure.” It might figure out that “azure” is typically used in similar contexts as “blue,” allowing it to describe the sky as azure rather than just blue.

This knack for generalization comes from the model’s deep understanding of language and how words are used in different situations. 

As a result, LLMs can produce responses that sound right but may not always be factually correct. Essentially, they’re creating new, believable content based on patterns they’ve learned.

At its core, the model operates without real-world context. It’s essentially crunching numbers and estimating the likelihood of one word following another, which is more about math than actual understanding.

To better understand and control these phenomena, Google researchers have introduced the Patchscopes framework, which aims to explore the AI’s internal mechanisms, offering clearer explanations of how these models process information.

Video source: YouTube/The AI Storm

Training Data Challenges in AI Models

AI hallucinations can also stem from problems in how these models are trained. If the training process is flawed, or the data is biased, or not comprehensive enough, it might not be well-prepared to handle some questions correctly.

Here’s a deeper look at how specific training data challenges can contribute to AI hallucinations:

Limited Data Access

Sometimes, AI struggles with understanding the subtle nuances of language, especially in areas like healthcare and banking, where strict privacy laws restrict access to essential data. When crucial information is hard to come by or tough to gather, AI can churn out responses that feel shallow or off-target.

Poor Data Quality 

When AI systems are trained on data containing errors, biases, or irrelevant information, they replicate these issues in their outputs. As a result, the AI-generated responses can be biased or completely incorrect, directly mirroring the deficiencies of their training data.

Rapidly Changing Information

In fast-moving fields like technology or current affairs, the relevance of training data can quickly become outdated. If the AI’s training data does not keep pace with current developments, it can cause AI to lag and provide responses that seem disconnected from current realities or incorrect.

The issues leading to AI hallucinations really underline how crucial it is for AI systems to be trained with high-quality and current data to ensure the reliability and accuracy of AI systems.

How Are Tech Companies Tackling the Challenge of AI Hallucinations?

Tackling hallucinations in AI chatbots is complex, primarily because these glitches are deeply embedded in how chatbots are designed. 

The unpredictability of text generation adds a layer of creativity; however, making the AI more predictable – by having it select the most likely words – can reduce these errors. This adjustment, though, might make conversations feel less dynamic and more repetitive.

According to recent data from the AI firm Vectara, chatbots can generate incorrect or imaginary responses about 3% to 27% of the time. The company maintains a so-called Hallucination Leaderboard on the GitHub developer platform, which tracks the frequency of these errors among popular chatbots during document summarization tasks.

Tech companies acknowledge these shortcomings. For instance, OpenAI noted that its latest iteration, GPT-4, released in March 2023, is significantly more accurate, showing a 40% improvement in providing factual responses compared to its predecessor, GPT-3.5. 

Similarly, Microsoft also addressed concerns about the accuracy of its AI tools. The company has made advancements in techniques that enhance the reliability of responses, including better grounding and fine-tuning of AI models to minimize the creation of fabricated answers.

Can AI Hallucinations be Eliminated?

Fully removing hallucinations from LLMs isn’t really feasible, but there are effective strategies to minimize them. One such strategy is retrieval augmented generation, which boosts the AI’s responses by pulling in verified information from sources like Wikipedia.

Another approach to reducing hallucinations is using causal AI, which helps the AI consider different scenarios and come to more comprehensive conclusions.

AI guardrails, similar to cybersecurity firewalls, are another practical solution. They help prevent the AI from creating baseless content by actively correcting mistakes as they occur and blocking potential threats.

Using high-quality and diverse training data, along with strict testing, helps a lot. Some experts recommend adopting journalistic standards to check AI-generated content against independent sources. Embedding the AI model within a larger system that continuously checks for consistency and factual accuracy can prevent many errors. This broader approach boosts reliability, helps companies comply with various standards, and avoids legal troubles.

A practical tip for everyday users is to try asking AI similar questions in slightly different ways to see if the answers stay consistent. This can give a good sense of how well the AI understands and how reliable its responses are.

The bottom line is that while we can’t entirely eliminate AI hallucinations, we can certainly manage them better. Managing the data well and setting clear benchmarks for what’s acceptable is key to reducing unwanted fabrications. Yet, having humans in the loop is crucial, as is providing oversight and ensuring the technology remains on track.

Video source: YouTube/360 SA

AI Hallucinations: Key Takeaways

It’s becoming increasingly important to grasp and handle AI hallucinations, especially as AI technologies seep into different sectors. 

These hallucinations stem from AI systems’ innate limitations and normal workings, emphasizing the pressing demand for strict supervision and creative approaches in their development and deployment. 

While completely eliminating these errors may not be feasible, implementing strategies such as retrieval augmented generation, causal AI, and the establishment of AI guardrails can significantly mitigate their occurrence. 

These measures, along with employing high-quality and diverse training data and integrating AI within systems that continuously verify accuracy, provide a layered defense against the propagation of misinformation. 

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.