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Nowadays, large language models (LLMs), the basis of popular AI technologies, have become irreplaceable tools for various fields, from writing texts to medical data processing. However, there is one serious problem: AI is prone to making mistakes much more often than we realize. The worst part is that many users are ready to accept such answers as the truth in the last resort.
Recent research by OpenAI has shown that even the most advanced models have a high error rate. For example, the “o1-preview” model in the SimpleQA test gave the correct answer only 42.7% of the time, meaning that more than half of the answers were incorrect. Other models, such as Anthropic's Claude-3.5-sonnet, performed even worse, with only 28.9% of the answers correct.
Why LLMs are prone to mistakes
To better understand the nature of LLM failures, it is worth delving into how they work. These neural networks create answers not by accurately reproducing information, but by generating new data based on the analysis of large amounts of text. Rather, they are like “compressed knowledge archives”: the model, using sophisticated algorithms, selects and transforms data to produce an understandable answer. However, during this process, some aspects of accuracy are inevitably lost. This means that even if the model uses reliable sources, you should not expect its answers to be completely error-free.
On the other hand, an important point is hallucinations (or “generation of fake information”), when the model invents facts. This is not a bug, but a characteristic of their architecture. The human brain can also create imaginary “universes” in the form of imagination or creativity. However, when this feature is inherent in a neural network, it can lead to serious problems in the real world. LLMs operate in a realm that extends far beyond reality, covering the domain of “fiction,” and this is not always useful in cases where reliable information is required.
Because LLMs not only generate answers, but also quote sources, present information in a structured way, add comments and details, all of which give them the appearance of credibility. The human brain, for its part, begins to perceive these answers as truth, because when something looks convincing, we are tempted to trust it without question.
This phenomenon is especially important in the context of critical thinking, which is essentially able to “switch off” when we are faced with systematically clear and well-structured answers. When a model confidently presents false information, accompanied by sources and details, critical thinking can easily go on hiatus.
Language models require a careful and critical approach. While the reliability of information has always been a priority, this aspect becomes more important than ever when working with AI. It is worth maintaining a certain distance to trust and checking any text generated by AI with great care.
Simple rules for users:
Why it is important to always check
LLMs have become widely used due to their ability to adapt and generate text quickly, but the reality is that their answers must be subjected to a thorough review. In today's world, where AI is becoming more and more integrated into our lives, mistakes can have catastrophic consequences.
AI is already being used in important areas ranging from content creation to decision-making in government agencies. And while the technology has the potential for further improvement, we need to remember that AI is a tool, not the ultimate master of all knowledge. And we, as users, have no right to abandon critical thinking just because “technology knows better.”
If we rely too much on LLMs without a critical eye, we risk ending up in a world where reality and fiction are so intertwined that even the truth becomes difficult to discern. AI tools can help us think, but they cannot replace thinking.
Questions and answers for the article about LLM mistakes
1. Why are LLMs prone to errors?
LLMs analyze large amounts of text and generate new data, but this process can lead to a loss of some aspects of accuracy. Also, LLMs can “hallucinate” or create fake data due to their architecture, similar to the creative process in the human brain.
2. How to use LLM carefully?
Users should always verify the information generated by LLM, seek confirmation from reliable sources, and be critical of the certainty and accuracy of the answers. It is important to pay attention to references and sources, as citations do not always guarantee accuracy.
3. Why is it important to always check the information from the LLM?
In the modern world, where AI is increasingly integrated into our lives, mistakes can have serious consequences. The use of LLM in important areas, such as government agencies or medicine, requires a responsible approach and critical thinking to ensure the accuracy and reliability of information.
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