Using Large Language Models to train smaller ones

After writing this, someone asked me if there's a way to avoid to constantly pay OpenAI API and still use at scale any GPT-3-based NLP pipeline, trying to keep the same quality. Of course, if you have enough budget for your use case, a fine-tuned GPT-3 (which now turned to be the even more powerful GPT-3.5) may still be the best choice in terms of quality. Said that, I think there are strategies to use GPT-3 more wisely, save some money and still get good results. For ABSA, sentiment analysis or any other NLP text classification task, you may consider to redesign your pipeline leveraging few-shot text classification. Indeed, recently significant improvements have been made public on this kind of tasks.