Hi. So I am a bit new to NLP and ML as a whole and I am looking to create a text classification model. I have tried it with deBERTa and the results are decent(about 70%) but I need more accuracy. Are Generstive models a better alternative or should I stick to smaller models like Bert or maybe even non-NN classifiers and work on better dataset quality?
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?