Seonglae ChoOut-of-Distribution
When data distributions differ, Transfer Learning or Meta Learning helps adapt models to new tasks.
OOD benchmark Dan Hendrycks
A Baseline for Detecting Misclassified and Out-of-Distribution...
We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions....
https://arxiv.org/abs/1610.02136
OOD generalization is crucial given the wide range of real-world scenarios in which these models are being used, while output diversity refers to the model’s ability to generate varied outputs and is important for a variety of use cases
RLHF generalizes better than SFT to new inputs, particularly as the distribution shift between train and test becomes larger. However, RLHF significantly reduces output diversity compared to SFT across a variety of measures, implying a tradeoff in current LLM fine-tuning methods between generalization and diversity.
arxiv.org
https://arxiv.org/pdf/2310.06452