AI (artificial intelligence), much like humans, is capable of making errors. It’s possible for large language models (LLMs), despite their level of complexity, to provide outputs that are erroneous or deceptive. Such a phenomenon is referred to as “AI hallucinations.” Depending on the particular application area, these hallucinations, which often originate from inadequacies in the training data format, have the potential to cause major downstream repercussions in the system.
In light of this, important concerns arise: What factors influence the incidence of these characteristics? And how can we be ready for a future in which AI will play an increasingly important role?
What Are AI Hallucinations, and Why Do They Occur?
AI hallucinations are flawed process outputs produced by LLMs. These models are trained on enormous databases of text and code, which enable them to recognise patterns and create a language that is reminiscent of human writing. Conversely, the LLM is susceptible to developing preconceptions and producing erroneous outputs if the training data is biassed, or inadequate.
Examples of AI Hallucinations
Even the most powerful AI models are susceptible to hallucinations. In 2023, Google’s Bard chatbot mistakenly said that the James Webb Space Telescope had taken photos of a planet beyond our solar system. Similarly, Microsoft’s chatbot gained attention after claiming affection for its users and confessing to spying on Bing employees. Aside from these examples, hallucinations often emerge as inaccurate forecasts, false positives, or false negatives.
Causes of AI Hallucination
LLMs are trained on extensive datasets, identifying patterns within this data to make predictions. However, errors in the training data can lead to inaccurate predictions and cause AI hallucinations. These hallucinations can arise from several common causes.
Faulty training data is a significant contributor. If the data used to train the LLM is incomplete, biassed, or inaccurate, the model will inherit these flaws and produce unreliable outputs. Data mismanagement can also lead to issues. Poorly classified data can be misinterpreted by the LLM, leading to incorrect conclusions.
Another cause is overfitting and underfitting. Overfitting occurs when the LLM memorises the training data too rigidly, while underfitting happens when the model fails to learn from the data effectively. Both scenarios hinder the model’s ability to adapt to new information. Misconceptions from the start also play a role; if the model is programmed with incorrect underlying assumptions, it will inevitably produce flawed results.
Future Predictions
AI hallucinations are growing as AI is being deployed in more domains. If an AI-powered medical system diagnoses incorrectly, it may prescribe incorrect treatments, endangering patients. In security, AI hallucinations might propagate incorrect information in critical domains like national defence, affecting international decisions. In finance, an AI model could mistakenly mark valid financial deals as fake, which would slow down operations and add extra problems that aren’t necessary. On the contrary, it could fail to detect actual fraudulent activity, leading to significant financial losses. These cases show that it’s imperative to take strong steps to reduce the risks of AI hallucinations.
Preparing for the Future
Given the potential dangers of AI hallucinations, it’s crucial to take steps to minimise their impact. One key strategy is to ensure the use of high-quality training data. The quality of LLM’s output hinges on the data it’s trained on. Rigorous data preparation is essential to ensure the data is accurate, up-to-date, unbiased, and relevant to the intended task.
Additionally, clearly defined expectations are vital. It’s important to explicitly define what the AI should and shouldn’t do when assigning a task. For instance, in content creation, providing examples of well-written and poorly-written texts can be beneficial. Filtering tools and probability thresholds can also be employed to guide the model towards more reliable outputs.
Another effective approach is the use of data models and templates. Predefined formats can channel the AI’s generation process. For written text, a template might include a title, introduction, headings, body paragraphs with specified word counts, and a conclusion. This helps the AI stay on track and reduces the likelihood of hallucinations.
Finally, and most importantly, human oversight remains crucial. As powerful as AI can be, it requires human supervision. Humans need to be involved in the process to examine each result and identify any potential errors. By quickly addressing these issues, we can ensure that AI hallucinations don’t become a roadblock on the path to technological progress.
There is no doubt that AI has a bright future ahead of it, but it’s important to be aware of the problems that lie ahead. By understanding AI hallucinations, and the methods to mitigate them, one can ensure that AI is a tool for positive change. Checking its reliability under the able guidance and supervision of capable and humane human beings. By carefully strategizing and dedicatedly advancing, we can harness the power of AI to shape a future defined by excellence, innovation and progress while also minimising the potential dangers linked to AI hallucinations.
Prateek Sethi is the founder of TRIP, a communication design house. Views expressed are personal.