I came here thinking I will learn how to make LLMs better. But leaving with more complicated questions: 1. Do I want LLMs to be trained with licensed data, tha

I came here thinking I will learn how to make LLMs better. But leaving with more complicated questions: 1. Do I want LLMs to be trained with licensed data, that's arguably well curated. Or, do I want LLM to scrape the web because it is more democratic in opinions? 2. If hallucination is not about training data but how LLM uses that data to extrapolate info that's not directly present in training data - can we teach it this skill to make better choices? 3. It's easy to define good data for facts. How to define good data for subjective topics? 4. For subjective topics, is it better to have separate LLMs trained with each theme of opinions or one big LLM with a mix of all opinions? 5. Is using LLM to improve its own training data truly helpful as the author claims? If yes - is this recursion method better or it's better to use multiple LLMs together? Dang! If I interview for a position that requires knowledge of AI - every question they ask will be answered with more questions. smh!