Exactly. It didn’t hallucinate even once in my tests. I used RAG and it gave me perfect to-the-point answers. But I know most people want more verbose outputs it’s just that it’s good for factual retrieval use cases.
It returns a JSON with function name and respective arguments which you can parse later in the program and call the function with those arguments given by the model.
My classification task is to classify a given essay into AI generated and human generated. And I need the answer to be between 0 and 1(both included) with 1 being AI generated and 0 being human generated.
Few-shot examples is a good idea for most classification tasks but I don’t think Generative LLMs can understand the more intricate semantic patterns to differentiate between the AI and human generated with just a few examples but I’ll try it once and let you know!
Btw do you think fine-tuning would be better?
What’s the correct prompt template for this specific model?