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Language Model Users Beware: 4 Pitfalls to Keep in Mind

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While language models like ChatGPT have been increasingly used in various tasks, it is important to be aware of their limitations. These can produce fabricated, conflicting, or outdated information, leading to inaccurate responses and potential risks. Factors such as data quality, algorithmic limitations, outdated knowledge, and contextual reliance can contribute to these issues. Users should exercise caution, verify information independently, and refrain from sharing sensitive data when interacting with language models.

Nowadays, language models like ChatGPT have been employed in a wide variety of tasks, ranging from fact-checking and email services to medical reporting and legal services.

While they are transforming our interaction with technology, it is important to remember that sometimes the information they give can be fabricated, conflicting, or old. As language models have this tendency to create false information, we need to be careful and aware of the problems that may arise when using them.

What Is a Language Model?

A language model is an AI program that can understand and create human language. The model is trained on text data to learn how words and phrases fit together to form meaningful sentences and convey information effectively.

The training is usually performed by enabling the model to predict the next word. After training, the model uses the learned ability to create text from a few initial words called prompts. For instance, if you provide ChatGPT with an incomplete sentence like “Techopedia is _____,” it will generate the following prediction: “Techopedia is an online technology resource that offers a wide range of articles, tutorials, and insights on various technology-related topics.”

The recent success of language models is primarily due to their extensive training in Internet data. However, while this training has improved their performance at many tasks, it has also created some issues.

Since the Internet contains incorrect, contradictory, and biased information, the models can sometimes give wrong, contradictory, or biased answers. It is, therefore, crucial to be cautious and not blindly trust everything generated by these models.


Hence, understanding the limitations of the models is vital to proceed with caution.

Hallucinations of Language Models

In AI, the term “hallucination” refers to the phenomenon where the model confidently makes incorrect predictions. It is similar to how people might see things that are not actually there. In language models, “hallucination” refers to when the models create and share incorrect information that appears to be true.

4 Forms of AI’s Hallucinations

Hallucination can occur in a variety of forms, including:

Fabrication: In this scenario, the model simply generates false information. For instance, if you ask it about historical events like World War II, it might give you answers with made-up details or events that never actually occurred. It could mention non-existent battles or individuals.

Factual inaccuracy: In this scenario, the model produces statements that are factually incorrect. For example, if you ask about a scientific concept like the Earth’s orbit around the Sun, the model might provide an answer that contradicts established scientific findings. Instead of stating the Earth orbits the Sun, the model might wrongly claim that the Earth orbits the Moon.

Sentence contradiction: This occurs when the language model generates a sentence that contradicts what it previously stated. For example, the language model might assert that “Language models are very accurate at describing historical events,” but later claim, “In reality, language models often generate hallucinations when describing historical events.” These contradictory statements indicate that the model has provided conflicting information.

Nonsensical content: Sometimes, the generated content includes things that make no sense or are unrelated. For example, it might say, “The largest planet in our solar system is Jupiter. Jupiter is also the name of a popular brand of peanut butter.” This type of information lacks logical coherence and can confuse readers, as it includes irrelevant details that are neither necessary nor accurate in the given context.

2 Key Reasons Behind AI’s Hallucinations

There could be several reasons that enable language models to hallucinate. Some of the main reasons are:

Data quality: Language models learn from a vast amount of data that can contain incorrect or conflicting information. When the data quality is low, it affects the model’s performance and causes it to generate incorrect responses. Since the models can not verify if the information is true, they may sometimes provide answers that are incorrect or unreliable.

Algorithmic limitations: Even if the underlying data is reliable, AI models can still generate inaccurate information due to inherent limitations in their functioning. As AI learns from extensive datasets, it acquires knowledge of various aspects crucial for generating text, including coherence, diversity, creativity, novelty, and accuracy. However, sometimes, certain factors, such as creativity and novelty, can take precedence, leading the AI to invent information that is not true.

Outdated Information

The language models like ChatGPT are trained on older datasets, which means they don’t have access to the latest information. As a result, the responses of these models may sometime be incorrect or outdated.

An example of how ChatGPT can present outdated information
When prompted with a question like “How many moons does Jupiter have?” NASA’s recent discovery indicates that Jupiter has between 80 and 95 moons. However, ChatGPT, relying on its data only up until 2021, predicts that Jupiter has 79 moons, failing to reflect this new finding.

This demonstrates how language models may provide inaccurate information due to outdated knowledge, making their responses less reliable. Additionally, language models can struggle to comprehend new ideas or events, further affecting their responses.

Therefore, when using language models for quick fact-checking or to get up-to-date information, it is essential to keep in mind that their responses may not reflect the most recent developments on the topic.

Impact of Context

Language models use previous prompts to enhance their understanding of user queries. This feature proves beneficial for tasks such as contextual learning and step-by-step problem-solving in mathematics.

However, it is essential to recognize that this reliance on context can occasionally lead to generating inappropriate responses when the query deviates from the previous conversation.

To get accurate answers, it is important to keep the conversation logical and connected.

Privacy and Data Security

Language models possess the capacity to utilize the information shared during interactions. Consequently, disclosing personal or sensitive information to these models carries inherent risks to privacy and security.

It is thus important to exercise caution and refrain from sharing confidential information when using these models.

The Bottom Line

Language models like ChatGPT have the potential to completely transform our interaction with technology. However, it is crucial to acknowledge the associated risks. These models are susceptible to generating false, conflicting, and outdated information.

They may experience “hallucinations” producing made-up details, factually incorrect statements, contradictory answers, or nonsensical responses. These issues can arise due to factors such as low data quality and inherent limitations of the algorithms employed.

The reliability of language models can be impacted by low data quality, algorithmic limitations, outdated information, and the influence of context.

Moreover, sharing personal information with these models can compromise privacy and data security, necessitating caution when interacting with them.


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Dr. Tehseen Zia
Tenured Associate Professor
Dr. Tehseen Zia
Tenured Associate Professor

Dr. Tehseen Zia has Doctorate and more than 10 years of post-Doctorate research experience in Artificial Intelligence (AI). He is Tenured Associate Professor and leads AI research at Comsats University Islamabad, and co-principle investigator in National Center of Artificial Intelligence Pakistan. In the past, he has worked as research consultant on European Union funded AI project Dream4cars.