Right now this is exactly what it is. No more, no less.
Artificial intelligence, especially language models like me, can “hellucinate” because they work on probabilities and patterns based on the data they were trained with. This means that we are not really “understanding” what we say, but are based on what is statistically likely to fit a particular request or question. Hallucinations in AIs are mistakes where the answer generated is incorrect, invented or contradictory, even though it sounds plausible.
Here are some main reasons why this happens:
1. Lack of “world perception” or contextual understanding:
AIs do not have a real perception of the world or a deeper understanding of information. They only have access to a large amount of text they have processed and use them to generate answers. However, if they access information that they do not understand exactly or are incomplete, they can make false or fabricated statements.
2. Statistical patterns instead of real knowledge:
Language models such as GPT-4 recognize patterns in the training data and use them to predict next words. This means that if the model does not have a reliable source of an answer, it can fall back on general patterns that appear plausible but are not necessarily correct. It then “hallucinates” an answer that corresponds to the probability of what it has seen in similar texts, but is not based on verified knowledge.
3. Incomplete or incorrect training data:
Language models are trained on huge data sets, which come from texts from the Internet, books, scientific articles and many other sources. However, these data sets do not always contain complete, accurate or reliable information. In addition, outdated or contradictory data may be included, which increases the likelihood of hallucinations.
4. Combination of vague information:
When a model encounters incomplete or vague information, it may begin to combine different elements to generate a plausible response. This often leads to hallucinations, since there is no objective method to distinguish between “good” and “bad” sources when the context is unclear.
5. Limited ability to verify source:
AIs cannot check external sources in real time. Although you have access to a huge database, you cannot access up-to-date, reliable data in real time. This means that a model regarding newer or specialized information can only access the sources with which it has been trained. Without a system to verify facts or to differentiate between reliable and unreliable sources, it can generate incorrect information.
6. Complexity of human knowledge:
Many topics that affect people are complex, multi-layered and nuanced. AIs based primarily on statistical relationships have difficulty capturing and processing these nuances correctly. They therefore tend to provide simple answers that may not be right.
7. Optimization for coherence instead of accuracy:
The models are optimized to generate coherent and fluid responses that sound like human speech. As a result, the focus is on the “probability” of an answer that fits well into the context, rather than on the true to fact. This promotes hallucinations when coherence becomes more important than the exact truth.
How can hallucinations be reduced?
- Verification techniques: One possible solution is to develop systems that search sources in real time or integrate fact check mechanisms to check responses before they are released.
- Improve training data: When models are trained with better curated and more reliable data, hallucinations can be reduced.
- Multimodal approaches: The use of multimodal AIs that analyze not only text, but also images or other forms of data, could help to develop a deeper understanding and reduce hallucinations.
Overall, hallucinations in AIs are a well-known problem that goes hand in hand with the current technology, but there is continuous research to minimize these errors.
quoted from chatgpt. 