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AI HALLUCINATIONS: WHAT THEY ARE AND WHY THEY HAPPEN

Grammarly
Updated on June 27, 2024AI


WHAT ARE AI HALLUCINATIONS?

AI hallucinations occur when AI tools generate incorrect information while
appearing confident. These errors can vary from minor inaccuracies, such as
misstating a historical date, to seriously misleading information, such as
recommending outdated or harmful health remedies. AI hallucinations can happen
in systems powered by large language models (LLMs) and other AI technologies,
including image generation systems.

For example, an AI tool might incorrectly state that the Eiffel Tower is 335
meters tall instead of its actual height of 330 meters. While such an error
might be inconsequential in casual conversation, accurate measurements are
critical in high-stakes situations, like providing medical advice.

To reduce hallucinations in AI, developers use two main techniques: training
with adversarial examples, which strengthens the models, and fine-tuning them
with metrics that penalize errors. Understanding these methods helps users more
effectively utilize AI tools and critically evaluate the information they
produce.



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EXAMPLES OF AI HALLUCINATIONS

Earlier generations of AI models experienced more frequent hallucinations than
current systems. Notable incidents include Microsoft’s AI bot Sydney telling
tech reporter Kevin Roose that it “was in love with him,” and Google’s Gemini AI
image generator producing historically inaccurate images.

However, today’s AI tools have improved, although hallucinations still occur.
Here are some common types of AI hallucinations:

 * Historical fact: An AI tool might state that the first moon landing happened
   in 1968 when it actually occurred in 1969. Such inaccuracies can lead to
   misrepresentations of significant events in human history.
 * Geographical error: An AI might incorrectly refer to Toronto as the capital
   of Canada despite the actual capital being Ottawa. This misinformation could
   confuse students and travelers looking to learn about Canada’s geography.
 * Financial data: An AI model could hallucinate financial metrics, such as
   claiming a company’s stock price rose by 30 percent in a day when, in fact,
   the change was much lower. Relying solely on erroneous financial advice could
   lead to poor investment decisions.
 * Legal guidance: An AI model might misinform users that verbal agreements are
   as legally binding as written contracts in all contexts. This overlooks the
   fact that certain transactions (for instance, real estate transactions)
   require written contracts for validity and enforceability.
 * Scientific research misinformation: An AI tool might cite a study that
   supposedly confirms a scientific breakthrough when no such study exists. This
   kind of hallucination can mislead researchers and the public about
   significant scientific achievements.


WHY DO AI HALLUCINATIONS HAPPEN?

To understand why hallucinations occur in AI, it’s important to recognize the
fundamental workings of LLMs. These models are built on what’s known as a
transformer architecture, which processes text (or tokens) and predicts the next
token in a sequence. Unlike human brains, they do not have a “world model” that
inherently understands history, physics, or other subjects.

An AI hallucination occurs when the model generates a response that’s inaccurate
but statistically similar to factually correct data. This means that while the
response is false, it has a semantic or structural resemblance to what the model
predicts as likely.

Other reasons for AI hallucinations include:


INCOMPLETE TRAINING DATA

AI models rely heavily on the breadth and quality of the data they are trained
on. When the training data is incomplete or lacks diversity, it limits the
model’s ability to generate accurate and well-rounded responses. These models
learn by example, and if their examples do not cover a wide enough range of
scenarios, perspectives, and counterfactuals, their outputs can reflect these
gaps.

This limitation often manifests as hallucinations because an AI model may fill
in missing information with plausible but incorrect details. For instance, if an
AI has been predominantly exposed to data from one geographic region—say, a
place with extensive public transportation—it might generate responses that
assume these traits are global when they aren’t. The AI isn’t equipped to know
that it’s venturing beyond the boundaries of what it was trained on. Hence, the
model might make confident assertions that are baseless or biased.


BIAS IN THE TRAINING DATA

Bias in the training data is related to completeness, but it is not the same.
While incomplete data refers to gaps in the information provided to the AI,
biased data means that the available information is skewed in some way. This is
unavoidable to some degree, given these models are trained largely on the
internet, and the internet has inherent biases. For example, many countries and
populations are underrepresented online—nearly 3 billion people worldwide still
lack internet access. This means the training data may not adequately reflect
these offline communities’ perspectives, languages, and cultural norms.

Even among online populations, there are disparities in who creates and shares
content, what topics are discussed, and how that information is presented. These
data skews can lead to AI models learning and perpetuating biases in their
outputs. Some degree of bias is inevitable, but the extent and impact of data
skew can vary considerably. So, the goal for AI developers is to be aware of
these biases, work to mitigate them where possible, and assess whether the
dataset is appropriate for the intended use case.


LACK OF EXPLICIT KNOWLEDGE REPRESENTATION

AI models learn through statistical pattern-matching but lack a structured
representation of facts and concepts. Even when they generate factual
statements, they don’t “know” them to be true because they don’t have a
mechanism to track what’s real and what’s not.

This absence of a distinct factual framework means that while LLMs can produce
highly reliable information, they excel at mimicking human language without the
genuine understanding or verification of facts that humans possess. This
fundamental limitation is a key difference between AI and human cognition. As AI
continues to develop, addressing this challenge remains crucial for developers
to enhance the trustworthiness of AI systems.


LACK OF CONTEXT UNDERSTANDING

Context is crucial in human communication, but AI models often struggle with it.
When prompted in natural language, their responses can be overly literal or out
of touch because they lack the deeper understanding humans draw from context—our
knowledge of the world, lived experiences, ability to read between the lines,
and grasp of unspoken assumptions.

Over the past year, AI models have improved in understanding human context, but
they still struggle with elements like emotional subtext, sarcasm, irony, and
cultural references. Slang or colloquial phrases that have evolved in meaning
may be misinterpreted by an AI model that hasn’t been recently updated. Until AI
models can interpret the complex web of human experiences and emotions,
hallucinations will remain a significant challenge.


HOW OFTEN DO AI CHATBOTS HALLUCINATE?

It’s challenging to determine the exact frequency of AI hallucinations. The rate
varies widely based on the model or context in which the AI tools are used. One
estimate from Vectara, an AI startup, suggests chatbots hallucinate anywhere
between 3 percent and 27 percent of the time, according to Vectara’s public
hallucination leaderboard on GitHub, which tracks the frequency of
hallucinations among popular chatbots when summarizing documents.

Tech companies have implemented disclaimers in their chatbots that warn people
about potential inaccuracies and the need for additional verification.
Developers are actively working to refine the models, and we have already seen
progress in the last year. For example, OpenAI notes that GPT-4 is 40 percent
more likely to produce factual responses than its predecessor.


HOW TO PREVENT AI HALLUCINATIONS

While it’s impossible to completely eradicate AI hallucinations, several
strategies can reduce their occurrence and impact. Some of these methods are
more applicable to researchers and developers working on improving AI models,
while others pertain to everyday people using AI tools.


IMPROVE THE QUALITY OF TRAINING DATA

Ensuring high-quality and diverse data is crucial when trying to prevent AI
hallucinations. If the training data is incomplete, biased, or lacks sufficient
variety, the model will struggle to generate accurate outputs when faced with
novel or edge cases. Researchers and developers should strive to curate
comprehensive and representative datasets that cover various perspectives.


LIMIT THE NUMBER OF OUTCOMES

In some cases, AI hallucinations happen when models generate a large number of
responses. For example, if you ask the model for 20 examples of creative writing
prompts, you might realize the result quality declines towards the end of the
set. To mitigate against this, you can constrain the result set to a smaller
number and instruct the AI tool to focus on the most promising and coherent
responses, reducing the chances of it responding with far-fetched or
inconsistent outcomes.


TESTING AND VALIDATION

Both developers and users must test and validate AI tools to ensure reliability.
Developers must systematically evaluate the model’s outputs against known
truths, expert judgments, and evaluation heuristics to identify hallucination
patterns. Not all hallucinations are the same; a complete fabrication differs
from a misinterpretation due to a missing context clue.

Users should validate the tool’s performance for specific purposes before
trusting its outputs. AI tools excel at tasks like text summarization, text
generation, and coding but are not perfect at everything. Providing examples of
desired and undesired outputs during testing helps the AI learn your
preferences. Investing time in testing and validation can significantly reduce
the risk of AI hallucinations in your application.


PROVIDE TEMPLATES FOR STRUCTURED OUTPUTS

You can provide data templates that tell AI models the precise format or
structure in which you want information presented. By specifying exactly how
results should be organized and what key elements should be included, you can
guide the AI system to generate more focused and relevant responses. For
example, if you’re using an AI tool to review Amazon products, simply copy all
the text from a product page, then instruct the AI tool to categorize the
product using the following example template:

Prompt: Analyze the provided Amazon product page text and fill in the template
below. Extract relevant details, keep information concise and accurate, and
focus on the most important aspects. If any information is missing, write “N/A.”
Do not add any information not directly referenced in the text.

 * Product Name: [AI-deduced product name here]
 * Product Category: [AI-deduced product category here]
 * Price Range: [AI-deduced price here] [US dollars]
 * Key Features: [concise descriptions here]
 * Pros [top 3 in bullet points]
 * Cons [top 3 in bullet points]
 * Overall Rating: [ranked on a scale of 1–5]
 * Product Summary: [2–3 sentences maximum]

The resulting output is much less likely to involve erroneous output and
information that does not meet the specifications you provided.


USE AI TOOLS RESPONSIBLY

While the strategies mentioned above can help prevent AI hallucinations at a
systemic level, individual users can learn to employ AI tools more responsibly.
These practices may not prevent hallucinations, but they can improve your
chances of obtaining reliable and accurate information from AI systems.

 * Cross-reference results and diversify your sources: Don’t rely solely on a
   single AI tool for critical information. Cross-reference the outputs with
   other reputable sources, such as established news organizations, academic
   publications, trusted human experts, and government reports to validate the
   accuracy and completeness of the information.
 * Use your judgment: Recognize that AI tools, even the most advanced ones, have
   limitations and are prone to errors. Do not automatically trust their
   outputs. Approach them with a critical eye and use your own judgment when
   making decisions based on AI-generated information.
 * Use AI as a starting point: Treat the outputs generated by AI tools as a
   starting point for further research and analysis rather than as definitive
   answers. Use AI to explore ideas, generate hypotheses, and identify relevant
   information, but always validate and expand upon its generated insights
   through human expertise and additional research.


CONCLUSION

AI hallucinations arise from the current limitations of LLM systems, ranging
from minor inaccuracies to complete fabrications. These occur due to incomplete
or biased training data, limited contextual understanding, and lack of explicit
knowledge.

While challenging, AI technology remains powerful and is continuously improving.
Researchers are working to reduce hallucinations, and significant progress has
been made. You can limit hallucinations by providing structured templates,
constraining output, and validating the model for your use case.

Explore AI tools with an open mind. They offer impressive capabilities that
enhance human ingenuity and productivity. However, use your judgment with
AI-generated results and cross-reference information with reliable sources.
Embrace the potential of AI while staying vigilant for hallucinations.


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