3 ms·
A Large Language Model is a tool and people have to learn to use it, just like any other tool. I have used GPT as a machine learning model in a way similar to t
by hooande 4y ago
A Large Language Model is a tool and people have to learn to use it, just like any other tool. I have used GPT as a machine learning model in a way similar to the grandparent comment, and I now have some understanding of when and why it hallucinates in certain situations [0]. I use that understanding to filter responses and tweak prompts. It took a lot of time and effort invested, but I was able to get a handle on it.
I know that a lot of people won't do this, and will just accept whatever an LLM says at face value. And again, that's true for any tool. But if you invest the time to understand the parameters and limitations of the model, it can be incredibly valuable.
[0] in my experience, GPT davinci is much more likely to hallucinate in (non-programming) situations that would be difficult for a human to explain. using the above example, it can easily handle a standard credit application. But it will be more likely to hallucinate reasoning for a rare case like someone with a very high income but a low credit score. YMMV, just sharing what I've seen
- ip26 4y agoSadly for me, that's the only thing I want it to do- ingest the entire internet's worth of data and make a well reasoned assessment about an edge case or subtle trends based on that vast trove.
- phatfish 4y agoYes, this is the same for me. The things ChatGTP gets right are often the "common" situations. It's possible you aren't aware of these so surfacing these more "common" facts can be helpful. But ask it a question about an edge case and it will hallucinate a plausible answer, and even double down when you point out a mistake. By that point it's faster to find an answer by using a few "traditional" search keywords and reading the content yourself. We can assume it will improve though, it's just not there yet for "edge cases" in it's training data.