I asked it for a list of restaurants in my area using google maps and it said there is a restaurant (Mug and Bean) in my area and even used a real address but this restaurant is not in my town. Its only in a neighboring town with a different street address
In: Technology
First off, I’d just like to define an word to add some context. Semantics is the study of meaning. In simple terms semantics refers to the meaning behind the words that you write and that you say. When you speak you naturally think about the meaning of your words. When you ask a LLM a question, you are logically expecting it to answer with a semantically coherent and correct response.
As others have mentioned, LLMs are just adding the next word based on the probability calculated from the context. However this probability is calculated in very complex ways in the background. LLMs seem to have the ability to generalize certain semantic information within their neural networks to the point where is seems to be able to reason and connect seemingly disconnected pieces of information. However this phenomena is not fully understood at the moment. This also means that when you ask it something it doesn’t know it will always give you it’s best guess based on the probability. Another weird pattern that you might see when using LLMs is that it might tell you it doesn’t know something even when it does, this is probably because the original training data probably had a certain piece of text that might have biased the model into answering in a particular way.
Artificial Intelligence is often used as a term when referring to LLMs and Machine Learning. However there are several other branches of AI that are actively being explored that I think are worth mentioning in this thread.
Knowledge Graphs is a different approach to semantic data analysis and usage. With knowledge graphs it’s easier to determine what the system knows and what the system doesn’t know. So it’s easier to keep the system from hallucinating. However knowledge graphs are usually harder to create and harder to use for more casual things.
Another interesting branch of AI is Logic programming. With logic programming you can determine the rules of a problem you are trying to solve and allow the system to interpret those rules to find a solution. With Logic programming you can solve complex issues, however, similar to knowledge graphs, using logic programming languages tends to require a lot of time, and isn’t really convenient for day to day use.
I believe future research into AI will combine these technologies in smart ways to leverage each of their strengths and weaknesses.
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