First, we formalize the problem definition.Weintroducetheconceptof"Idon'tknow (idk) responses" and in this context, honesty necessitates that an aligned LLM provides idk responses for unknown questions and correct responses for known questions.

To remedy this, we introduce temporal certificate sets, a general notion for capturing the intrinsic temporal understanding length associated with a broad range of video understanding tasks & datasets.

Motivated by applications of large embedding models, we study differentially private (DP) optimization problems under sparsity of individual gradients. We start with new near-optimal bounds for the classic mean estimation problem but with sparse data, improving upon existing algorithms particularly for the high-dimensional regime.

Extensive experiments on various datasets show that our approach outperforms state-of-the-art methods, enhancing the model's

MolGroup achieves this by utilizing a routing mechanismoptimized through abi-level optimization framework.