Recall-oriented training losses (TruncR, c-Div with alpha>1, and lambda-PR) make language models more tunable through temperature, improving precision-recall trade-offs beyond NLL plus temperature scaling.
(57) In this regime, that is, for large values of λ, αλ reflects the model’s quality, as it converges to Precision whenλ→+∞
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Improving Diversity in Language Models: When Temperature Fails, Change the Loss
Recall-oriented training losses (TruncR, c-Div with alpha>1, and lambda-PR) make language models more tunable through temperature, improving precision-recall trade-offs beyond NLL plus temperature scaling.