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TheUnreliabilityofExplanationsinFew-shot PromptingforTextualReasoning
However, text-davinci-002 is able to benefit more substantially. We further show that explanations generated by the LLMs may not entail the models' predictions norbefactually grounded intheinput, evenonsimple tasks with extractive explanations. However, these flawed explanations can still be useful as a way to verify LLMs' predictions post-hoc.
EfficientGeneralization withDistributionallyRobustLearning
Consider a general formulation of the distributionally robust learning (DRL) problem of active interest.
Topology-Preserving Deep Image Segmentation
Xiaoling Hu, Fuxin Li, Dimitris Samaras, Chao Chen
Neural Information Processing Systems http://nips.cc/
Roblox child safety warning after Nebraska kidnapping case
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An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Wei Deng, Xiao Zhang, Faming Liang, Guang Lin
Neural Information Processing Systems http://nips.cc/
