Federico Barbero is a Research Scientist at Google DeepMind working on understanding and improving LLMs from first principles.
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Referenced in coverage
Perplexity is an unsuitable metric for model selection because models with low perplexity can make incorrect predictions, and perplexity does not always favor more accurate models over less accurate ones.
“Co-author of research on perplexity limitations showing that increased model confidence must be accompanied by commensurate accuracy gains.”