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Negative Log-Likelihood (NLL) has long been the go-to objective for classification and SFT, but is it universally optimal? We explore when alternative objectives outperform NLL and when they don't, based on two key factors: the objective's prior-leaningness and the model's capability. 📄 Paper: https://t.co/HbGXy60fzZ 💻 Code: https://t.co/XtoFiok4F7 (1/n)
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"text": "Negative Log-Likelihood (NLL) has long been the go-to objective for classification and SFT, but is it universally optimal? \n\nWe explore when alternative objectives outperform NLL and when they don't, based on two key factors: the objective's prior-leaningness and the model's capability.\n\n📄 Paper: https://t.co/HbGXy60fzZ\n💻 Code: https://t.co/XtoFiok4F7\n\n(1/n)",
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