Lately, models like GPT and other transformers have been showing impressive results, but their inner workings still remain a mystery to most people. What are the core principles behind how these models are trained and generate text? What typical limitations arise when using them—like issues with factual accuracy or a tendency to repeat themselves? In your opinion, what approaches could help mitigate these limitations?
How do language models work and what are their limitations when generating text?
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What metrics do you usually use to evaluate the factual accuracy of generated text, and how can they be integrated into the model training process?