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Transformer uncertainty is now reliably calibrated with linear-time attention.
The empirical analysis reveals that larger models may excel in generating narratives but often fail to maintain coherence and depth, exposing a critical trade-off in LLM storytelling capabilities.
GenRec shows that LLM-backed recommenders can outperform traditional models with significantly less training data, revolutionizing the recommendation landscape.
