It's well known that different kinds of generative AI can "make things up" — a phenomenon known as hallucination, where the AI produces information that isn't grounded in the provided context or reality.
Hallucinations in the AI-generated outputs are an essential issue to address, especially in applications where reliability is critical. These inaccuracies can lead to misleading information, potentially affecting trust, content quality, and decision-making processes.
To better understand how hallucinations manifest, let’s break down an example. Below, we compare the original source context on the left with its corresponding generative AI output, prompted with “Explain the key characteristics of the Renaissance period in simple terms,” on the right, illustrating where factual consistencies and discrepancies might arise:
Notice that the AI's output simplifies the historical context by stating “1300s” instead of “14th century” and uses more casual language like "big developments" and "things like" which aren't in the source text. While the core information remains similar, the AI has hallucinated specific details about "studying ancient Greek and Roman works" that aren't mentioned in the original text, demonstrating how AI can generate plausible but fabricated content.
Let's compare source text with generated text to detect potential hallucinations.
Find answers to common questions about AI Hallucination Checker.
AI hallucinations occur when generative AI models produce information that is factually incorrect or not grounded in the provided context. These fabricated details may appear plausible but do not align with the original source material.
Large language models compose responses that are statistically likely, based on patterns in their training data as well as the additional fine-tuning techniques such as reinforcement learning from human testers’ feedback. Still, their internal workings are still not precise, as admitted by the experts; hence, it is also not precisely clear how hallucinations are happening. Still, current literature suggests that they might be arising from factors such as incomplete or inconsistent training data, limitations in the model’s ability to understand the query/prompt’s context, and a lack of real-world knowledge/fact.
AI hallucinations can pose significant problems when the content is used in scenarios where accuracy is critical, such as reporting, documentation, or research. Misinformation generated by the AI could mislead users, damage trust, or lead to incorrect decisions. Therefore, it is essential to ensure that AI outputs are fact-checked and aligned with reliable sources.
AI hallucinations can pose significant problems when the content is used in scenarios where accuracy is critical, such as reporting, documentation, or research. Misinformation generated by the AI could mislead users, damage trust, or lead to incorrect decisions. Therefore, it is essential to ensure that AI outputs are checked and aligned with reliable sources.
In EdTech, AI-generated content is increasingly being used for educational materials. Hallucinations in this context could lead to misleading information being delivered to learners, undermining their learning experience. Hence, ensuring factual consistency is vital to maintaining educational integrity and trust in AI-driven learning tools.
Our method addresses a crucial need in today's world of AI-generated content: verifying whether AI-generated text stays true to its source material.
Think of it like a ‘fact-checking assistant’ that measures how well a rewrite maintains the factual integrity of the original text. The core idea behind it is that two pieces of text that convey the same facts should be recognised as similar, even if they use different words or phrasing.
This kind of tool becomes particularly valuable in checking if AI-generated summaries accurately reflect source documents. The end result is a numerical score that tells you how well the generated text preserved the facts from the original. This helps identify when AI-generated content might be straying from the facts of the source material, which is crucial for maintaining accuracy and trustworthiness in automated content creation.
At the core of AI-powered tool is the ability to turn your documents into courses while keeping them consistent with the original content and the context. With our approach, we help maintaining your content reliable, mitigating the risk of AI-generated inaccuracies or hallucinations. This means your courses stay true to their source, so you can focus on delivering quality learning experiences to engage your learners.
