the most critical challenge seems to be the ‘artificial hallucinations‘ or ‘AI hallucinations‘. The term artificial hallucinations regard a generative AI tool that produces meaningless or inaccurate results based on non-existent or imperceptible patterns.
This phenomenon can adversely affect the real world, such as in the dissemination of content of various kinds. More specifically, these inaccuracies can lead to the dissemination of incorrect information and undermine the credibility of the sources that publish this AI-produced content. Some sectors may suffer more than others, such as scientific and academic research.
There are several online resources that show step by step how to use large language models (LLM) to write a research paper. Using AI is certainly an effective method that saves time and costs when searching for scientific sources and drafting the article. The aim shouldn’t be to criticise the use of artificial intelligence to create entire research papers from the outset, but to explore the potential of AI in research writing, striking a balance between exploiting the advantages of these tools and mitigating their risks.
However, sometimes AI algorithms produce outputs that are not based on training data, are incorrectly decoded by the transformer, or do not follow any identifiable pattern. In other words, it ‘hallucinates’ the response. While many of these issues have since been addressed and resolved, it’s easy to see how, even in the best of circumstances, the use of AI tools can have unforeseen and undesirable consequences.
A Practical Feedback
To better understand, I decided to interact directly with three AI tools and ask them to explain the concept of artificial hallucination to me. After getting some answers, I asked them to provide me with scientific studies on the subject, so that I could explore the topic further. All three provided me with several titles, with their authors, journal, and year of publication. After personally verifying that they didn’t exist, I pointed out to them that none of those studies were published and the answers I received were more or less similar. I tried to summarise all the answers as follow by using their words.
“I apologize for the mistake. As an AI language model, I don’t ‘invent’ information intentionally. However, I do generate answers based on patterns in the data I have been trained on, and occasionally mistakes or inaccuracies can occur.
It would have been more appropriate for me to acknowledge that I couldn’t find specific studies matching the descriptions I provided. I apologize for not doing so and for any frustration or confusion this may have caused.
In the future, I will strive to be more transparent about the limitations of the information I provide.
It’s essential for users to approach the information provided by AI models, like me, with a critical mindset and to verify information, when necessary, especially for critical or sensitive matters. While I aim to assist users to the best of my abilities, I am not a substitute for professional advice or expertise.
Ultimately, it’s important for users to exercise discernment and critical thinking when interacting with AI models or any form of technology, and to recognize their limitations.”
These artificial intelligence limitations could be solved in a short time, but until then we can’t underestimate the problem and wait for a solution. Understanding the limits and acting accordingly must be a priority for everyone.
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